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OFFEN. DIVERS. INKLUSIV.
Thinking the Future of Organizations
Leonhard Dobusch
Professor für Organisation
Agenda UIBK Digital Leadership Club
Universität Innsbruck, 21. April 2023
Fortschritt vs. Fortschreiben
First. To point out, […] the great loss
which the whole country is suffering
through inef
fi
ciency in almost all of our
daily acts.
Second. […] the remedy for this inef
fi
ciency
lies in systematic management, rather than
in searching for some unusual or
extraordinary man.
Third. To prove that the best management is
a true science, resting upon clearly de
fi
ned
laws, rules, and principles, as a foundation.
“
Taylor, F. (1911). Principles
of Scientific Management
Quelle: https://youtu.be/OtoAAoTAkAM
A
Human
Being
is a
Tool.
It is
important
to
measure
and to
record
every-
thing
Bild: Álvaro Ibáñez, CC-BY 2.0, https://nl.wikipedia.org/wiki/Bestand:Amazon_Espa%C3%B1a_por_dentro_(San_Fernando_de_Henares).JPG
‣ „Picker“ legen Bestellungen in Trolleys
‣ Amazons Software kalkuliert den ef
fi
zientesten
Gehweg um den Trolley zu füllen…
‣ …und instruiert die Picker von einem Regal zum
nächsten.
‣ Management erhält „Echtzeit“-Informationen…
‣ …und kann Textnachrichten verschicken, die zu
rascherem Arbeiten ermahnen.
‣ Eingebettet in kontinuierliche
Verbesserungsprozesse (Kaizen, KVPs)
Quelle: https://www.salon.com/2014/02/23/worse_than_wal_mart_amazons_sick_brutality_and_secret_history_of_ruthlessly_intimidating_workers/
Neo-Taylorismus bei Amazon:
Die Zukunft von Gestern
Source: https://hbr.org/1965/07/human-relations-or-human-resources
[F]ocus attention on all organization
members as reservoirs of untapped
resources.
“ Miles, R.E. (1965)
Given these demographic changes,
most organizations recognize that
there is no turning back with respect
to diversity, that diversity is a fact
rather than a fad, and that companies
that do a good job with diversity will
gain a competitive edge over those
that do not.”
“
Ragins & Gonzalez
(2003, p. 126)
Report „Workforce 2000“ (1987)
13
Screenshot links aus dem Jahr 2016. Im Vergleich dazu Webseite 2020:
https://www.bmwgroup.jobs/de/de/ueber-uns/diversity.html
14
Screenshot links aus dem Jahr 2020. Im Vergleich dazu Webseite 2023:
https://www.bmwgroup.jobs/de/de/ueber-uns/diversity.html
Diversity
zwischen
Business
und Moral
Case
Offenheit als Lösung?
How smart companies are
opening up strategic initiatives
to involve front-line employees,
experts, suppliers, customers,
entrepreneurs, and even
competitors.
“
Ein Beispiel für Offenheit.
Beispiellose Offenheit.
“Imagine a world in which every single
human being can freely share in
the sum of all knowledge.”
Quelle: http://www.taz.de/!5127514/
Quelle: http://derstandard.at/2000022296889/Studie-Wikipedia-wird-vom-reichen-Westen-dominiert
The average Wikipedian on the English
Wikipedia is (1) white, (2) male, (3)
technically inclined, (4) formally educated,
(4) an English speaker (native or non-
native), (5) aged 15–49, (6) from a majority-
Christian country, (7) from a developed
nation, (8) from the Northern Hemisphere,
and (9) likely employed as a white-collar
worker or enrolled as a student rather than
being employed as a blue-collar worker.
“
Wikipedia:Systemic Bias,
https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias
Quelle: http://www.tagesanzeiger.ch/leben/gesellschaft/Der-Schwarm-bei-Wikipedia-schrumpft/story/11486176
Source: https://stats.wikimedia.org/#/de.wikipedia.org/contributing/active-editors/normal|line|all|(page_type)~content*non-content|monthly
Aktive Autorinnen und Autoren in der deutschsprachigen Wikipedia
Quelle:
https://de.wikipedia.org/wiki/Datei:Artikelwachstum_in_der_deutschsprachigen_Wikipedia.png
Artikelwachstum in der deutschsprachigen Wikipedia
Fehlende Vielfalt trotz radikaler Offenheit?
Paradoxe Performativität von Offenheit?
Non-performatives
describes the “reiterative
and citational practice by
which discourse” does not
produce “the effects that it
names” (Butler 1993: 2)
“
Ahmed, S. (2012, p. 117)
The seeds of closure are
always already present
within the open, but the
language of openness
doesn’t allow us to gain any
traction on that closure.
“
Tkacz, N. (2020)
The seeds of closure are
always already present
within the open, but the
language of openness
doesn’t allow us to gain any
traction on that closure.
“
Tkacz, N. (2020)
Fehlende Vielfalt trotz radikaler Offenheit?
wegen
Bild: https://commons.wikimedia.org/wiki/File:Being_any_Gender_is_a_drag_-_World_Pride_London_2012_(7527764372).jpg,, CC BY 2.0, by xJason.Rogersx
Spiegelbild gesellschaftlicher (Geschlechter-)Verhältnisse?
Quelle: https://commons.wikimedia.org/wiki/File:Wikipedia_Zero_1_Mumbai_Guy_on_phone.jpg
Spiegelbild gesellschaftlicher (Geschlechter-)Verhältnisse?
Usability: Wiki-Syntax
Quelle: https://commons.wikimedia.org/wiki/File:Editing_Wikipedia_p_11,_wiki_markup_illustration_1.png
0
25
50
75
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
edit
count
in
percent
per
year
in
each
user
group
Anonymous users Bots
Registered users
Mehr Edits von
Algorithmen
(»Bots«):
Aus: Müller-Birn, C./Dobusch, L./Herbsleb, J. D. (2013): Work-to-rule: the
emergence of algorithmic governance in Wikipedia. Proceedings of the
6th International Conference on Communities and Technologies (C&T
’13), ACM, 80–89.
Wenn Deine Gruppe aus
neun hilfreichen und
hö
fl
ichen Mitgliedern und
einem unhö
fl
ichen,
sexistischen und lauten
Mitglied besteht, dann
werden die meisten Frauen
wegen dieses einen
Mitglieds fernbleiben.
“
Valeria Aurora (2002), http://tldp.org/
HOWTO/Encourage-Women-Linux-
HOWTO/, Übersetzung L.D.
Pfadabhängigkeit (fehlender) Diversität?
Quelle: Sydow et al. (2009, p. 692)
Wikipedia-
spezi
fi
sch
Gesamt-
gesellschaftlich
Geschlossenheit
Hackerkultur
Trolle
»Bots«
0
25
50
75
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
edit
count
in
percent
per
year
in
each
user
group
Anonymous users Bots
Registered users
Figure 3: Development of edits per user group (registered user,
anonymous user, bot) in the Wikipedia’s administrative names-
pace 4.
continuously increasing in number. Since bots have shown their
usefulness for a wide variety of tasks in the main namespace, their
scope has steadily expanded, and more edits have taken place in
other namespaces.
This contradicts a community guideline that suggests the avoidance
of editing activities of bots outside the article namespace. However,
in 2012, these “outside” edits accounted for over 40 percent of all
bot edits. This emergence of bot activity all over the community
project is an indication of the growing importance of these “lit-
tle helpers” for the community’s activities. This relates to a study
that analyzed the diversification of human edits over the different
namespaces. In 2001, about 90 percent of all edits were carried
out in the article namespace, but in 2006, this number had already
decreased to 70 percent [15]. We assume that the change in the
community engagement of bot operators also expanded the reach
of bot edits. More interestingly, while human edits slowed down in
Wikipedia’s community space, edits carried out by bots increased
as shown in Figure 3. In this administrative space, 20 different bots
have been active on average (disregarding wikilink-bots).
In the next part of our analysis, we specifically look at the types of
activities bots carry out. Our interest is twofold: first, we classify
tasks executed by bots in order to understand their relatedness to
existing social governance mechanisms. Second, we examine our
assumption of increasingly algorithmic rule enforcement by bots.
We collected task descriptions from bots’ user pages to examine
the kinds of activities in which bots are participating in the Wiki-
pedia community. In single, doubtful cases we matched edits with
their task descriptions to identify discrepancies and exclude those
activities. Based on these data, we defined general activity types
that are indicated in the first column of the table 1. These general
activity types were defined in three steps. During the first round,
we coded existing task descriptions collaboratively (around 100)
until we had an almost stable set of activities. In the second round,
we separately coded the remaining task descriptions. In the third
round, we checked the assigned codes and compared them with
our own decisions, and collaboratively coded all task descriptions
that needed new activity types. In order to create a shared under-
standing of existing activity types, the second and third rounds were
an iterative process. Newly introduced activity types were always
cross-validated over the whole data set.
We clustered the manually defined sets of activities in activity types
(cf. second column of the table 1) and identified three foci of bot
activities (cf. fifth column of the table 1): (1) the content focus, (2)
the task focus, and (3) the community focus.
The first category contains mainly bots that are active in the article
namespace. These bots are created primarily to support the curat-
ing activities of their operators (for example, by using Autowiki-
browser – a semi-automated MediaWiki editor13
) or to connect dif-
ferent language versions of a page through interwiki-links. The
second category comprises bots that are used to support the main-
tenance work of editors by compiling working lists or by informing
editors about existing status changes on articles. The third category
- the community focus - refers to activities that are rather unrelated
to encyclopedic articles; they are more related to community rules
and their enforcement.
Four bots have a community focus: the CopperBot, GiftBot, Items-
bot and xqbot. The CopperBot is the German equivalent to the
HagermanBot of the English Wikipedia [8] that is responsible for
signing unsigned comments on discussion pages. The main task of
the Itemsbot was welcoming new users to the German Wikipedia
by leaving a message on their personal discussion pages. Probably
because of the aforementioned community consensus against bot
welcome messages, the bot stopped working within two months.
In 2008 and 2009, the operator of the Giftbot requested a bot flag
for her bot in order to correct spelling mistakes. In both cases, the
request was denied. In July 2010, the third request was successful.
This time, the bot tasks included the removal of processed flagged
revision requests, the dissemination of a newsletter that contains
information on new edits on pages such as polls, and requests for
banning users as well. All these activities were much more fo-
cused on specific community needs. We assume that the operator
of Giftbot learned much more about existing rules and guidelines
over time and was therefore much better able to meet the needs of
her fellows.
The last of the four community bots is introduced in more detail in
the next section. We show in an exemplary way how the activity
set employed by this bot changes over time.
5.3.1 Example: xqbot
In October 2008, the editor applied for a bot flag for her xqbot in
order to request speedy deletions of orphan pages14
or remains of
moved pages. In November 2008, the bot flag was assigned and
the bot started working. Soon after this, the bot activities included
over ten different tasks such as correcting double redirects, fixing
links on disambiguation pages, adding missing references tags in
articles, and the setting of interwiki-links. All these tasks were
mainly focused on quality improvements to encyclopedic articles.
In 2010, the focus changed in terms of additional tasks. This was
motivated mainly by a procedural problem that occurred during an
administrator re-election.
In January 2010, one participant initiated a discussion by question-
ing the procedure to take care of obsolete votes [31], [32]. The
13
http://en.wikipedia.org/wiki/Wikipedia:AutoWikiBrowse
14
Orphan pages on Wikipedia are articles that have no or very few
incoming links.
Usability
Spiegelbild der
Geschlechterverhältnisse
Zugang
zum Internet
Offenheit
Pfadabhängigkeiten
Mögliche Gründe
für Exklusion
in Wikipedia
Wikipedia-
spezi
fi
sch
Gesamt-
gesellschaftlich
Geschlossenheit
Hackerkultur
Trolle
»Bots«
0
25
50
75
100
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
edit
count
in
percent
per
year
in
each
user
group
Anonymous users Bots
Registered users
Figure 3: Development of edits per user group (registered user,
anonymous user, bot) in the Wikipedia’s administrative names-
pace 4.
continuously increasing in number. Since bots have shown their
usefulness for a wide variety of tasks in the main namespace, their
scope has steadily expanded, and more edits have taken place in
other namespaces.
This contradicts a community guideline that suggests the avoidance
of editing activities of bots outside the article namespace. However,
in 2012, these “outside” edits accounted for over 40 percent of all
bot edits. This emergence of bot activity all over the community
project is an indication of the growing importance of these “lit-
tle helpers” for the community’s activities. This relates to a study
that analyzed the diversification of human edits over the different
namespaces. In 2001, about 90 percent of all edits were carried
out in the article namespace, but in 2006, this number had already
decreased to 70 percent [15]. We assume that the change in the
community engagement of bot operators also expanded the reach
of bot edits. More interestingly, while human edits slowed down in
Wikipedia’s community space, edits carried out by bots increased
as shown in Figure 3. In this administrative space, 20 different bots
have been active on average (disregarding wikilink-bots).
In the next part of our analysis, we specifically look at the types of
activities bots carry out. Our interest is twofold: first, we classify
tasks executed by bots in order to understand their relatedness to
existing social governance mechanisms. Second, we examine our
assumption of increasingly algorithmic rule enforcement by bots.
We collected task descriptions from bots’ user pages to examine
the kinds of activities in which bots are participating in the Wiki-
pedia community. In single, doubtful cases we matched edits with
their task descriptions to identify discrepancies and exclude those
activities. Based on these data, we defined general activity types
that are indicated in the first column of the table 1. These general
activity types were defined in three steps. During the first round,
we coded existing task descriptions collaboratively (around 100)
until we had an almost stable set of activities. In the second round,
we separately coded the remaining task descriptions. In the third
round, we checked the assigned codes and compared them with
our own decisions, and collaboratively coded all task descriptions
that needed new activity types. In order to create a shared under-
standing of existing activity types, the second and third rounds were
an iterative process. Newly introduced activity types were always
cross-validated over the whole data set.
We clustered the manually defined sets of activities in activity types
(cf. second column of the table 1) and identified three foci of bot
activities (cf. fifth column of the table 1): (1) the content focus, (2)
the task focus, and (3) the community focus.
The first category contains mainly bots that are active in the article
namespace. These bots are created primarily to support the curat-
ing activities of their operators (for example, by using Autowiki-
browser – a semi-automated MediaWiki editor13
) or to connect dif-
ferent language versions of a page through interwiki-links. The
second category comprises bots that are used to support the main-
tenance work of editors by compiling working lists or by informing
editors about existing status changes on articles. The third category
- the community focus - refers to activities that are rather unrelated
to encyclopedic articles; they are more related to community rules
and their enforcement.
Four bots have a community focus: the CopperBot, GiftBot, Items-
bot and xqbot. The CopperBot is the German equivalent to the
HagermanBot of the English Wikipedia [8] that is responsible for
signing unsigned comments on discussion pages. The main task of
the Itemsbot was welcoming new users to the German Wikipedia
by leaving a message on their personal discussion pages. Probably
because of the aforementioned community consensus against bot
welcome messages, the bot stopped working within two months.
In 2008 and 2009, the operator of the Giftbot requested a bot flag
for her bot in order to correct spelling mistakes. In both cases, the
request was denied. In July 2010, the third request was successful.
This time, the bot tasks included the removal of processed flagged
revision requests, the dissemination of a newsletter that contains
information on new edits on pages such as polls, and requests for
banning users as well. All these activities were much more fo-
cused on specific community needs. We assume that the operator
of Giftbot learned much more about existing rules and guidelines
over time and was therefore much better able to meet the needs of
her fellows.
The last of the four community bots is introduced in more detail in
the next section. We show in an exemplary way how the activity
set employed by this bot changes over time.
5.3.1 Example: xqbot
In October 2008, the editor applied for a bot flag for her xqbot in
order to request speedy deletions of orphan pages14
or remains of
moved pages. In November 2008, the bot flag was assigned and
the bot started working. Soon after this, the bot activities included
over ten different tasks such as correcting double redirects, fixing
links on disambiguation pages, adding missing references tags in
articles, and the setting of interwiki-links. All these tasks were
mainly focused on quality improvements to encyclopedic articles.
In 2010, the focus changed in terms of additional tasks. This was
motivated mainly by a procedural problem that occurred during an
administrator re-election.
In January 2010, one participant initiated a discussion by question-
ing the procedure to take care of obsolete votes [31], [32]. The
13
http://en.wikipedia.org/wiki/Wikipedia:AutoWikiBrowse
14
Orphan pages on Wikipedia are articles that have no or very few
incoming links.
Usability
Spiegelbild der
Geschlechterverhältnisse
Zugang
zum Internet
Offenheit
Pfadabhängigkeiten
Mögliche Gründe
für Exklusion
in Wikipedia EXKLUDIERENDE
OFFENHEIT
Offen für alle ist nicht offen genug.
Markenrechte
Serverinfrastruktur
Softwareentwicklung
Erstellung von Inhalten
Löschung von Inhalten
Arbeitsteilung
Foundation/Community
Arbeitsteilung
Foundation/Community
Arbeitsteilung
Foundation/Community
Community-
ManagerInnen?
Offen für alle ist nicht offen genug.
Corporate Edition
M
U
S
T
E
R
MUSTER:
Schwärzen in Lebenslauf
Anonymisierung: Neutrale Stelle
Lebenslauf Marianne Mustermann
Persönliche Daten
Marianne Mustermann
Beispielallee 56
12345 Beispielshausen
Tel.: 123 - 45678990
Mobil: 0123 – 456890
E-Mail: inskdjkerwurhr
Geburtsdatum: 23.34.2340
Veburtsort: hseurhuserzjk
Bürokauffrau
Fachspezifische Erfahrungen
01.09.2007 – 31.05.2011 Assistentin der Geschäftsführung, Bereich Groß- und
Einzelhandel
Beispiel GmbH, Berlin
Aufgaben:
Vorbereitung der Buchhaltung
Erstellung von Budgets
Protokollführung bei Sitzungen und
Besprechungen
31.05.2006 - 31.07.2007 Projektassistentin
Muster AG, Berlin
Aufgaben:
Terminplanung und -überwachung
Erstellen von Präsentationen
Berufsausbildung
01.09.1977 – 15.07.1980 Muster AG, Berlin
Bürokauffrau
Schulbildung
1995 Georg-Büchner-Schule, Berlin
Realschulabschluss
Kenntnisse und Fähigkeiten
Sprachkenntnisse Englisch, Grundkenntnisse
EDV-Kenntnisse Gute MS-Office-Kenntnisse, sicherer Umgang mit dem Internet
Sonstige Führerschein Klassen B
Hobbys Schwimmen, Segeln, Volleyball
Beispiel: Schwärzung
von Bewerbungsbögen
49
Gleichheitsorientiert
"Identity-blind"
Beispiel: Gesetzliche
Geschlechterquoten
51
Differenzorientiert
"Identity-conscious"
52
Geschlechterquote
tritt in Kraft
Beispiel: Gesetzliche
Geschlechterquoten
53
nigten Königreich (+16,6 Prozentpunkte) verfügen nur zwei Länder ohne Quotenregelung über einen überdurchschnittlichen
Anstieg bei der Frauenquote. Von den restlichen Ländern mit Quotenregelung findet sich Spanien (+14,2 Prozentpunkte)
knapp unter dem Durchschnitt. Lediglich Norwegen hatte in diesem Zeitraum nur einen Anstieg von 1,3 Prozentpunkten,
jedoch trat dort die Quotenregelung schon 2006 in Kraft, 2010 war bereits ein Frauenanteil von 38,9 % erreicht. Das mit
Abstand größte Minus von 2010 auf 2018 fällt dem derzeitigen EU-Ratspräsidentschaftsland Rumänien mit -10,3 Prozent-
punkten zu.
Veränderung des Frauenanteils in den Aufsichts- oder Verwaltungsräten
Europa 2010–201846
Abbildung 2: Veränderung des Frauenanteils in den Aufsichts- und Verwaltungsräten, Angaben in Prozentpunkten47
32 32
29
22 21
18 17 17 16 16
14
10 10 10 10 9 9 9
7 7 6 6
3 3 3 2 2 1
1 1
-1 -2
-10
Quotenregelung keine Quotenregelung
EU-28: +15
Quelle: Frauen.Management.Report.2019 Aufsichtsratsquote – das Jahr danach, https://www.arbeiterkammer.at/
interessenvertretung/wirtschaft/betriebswirtschaft/AK.Frauen.Management.Report.2019.pdf
Beispiel: Gesetzliche
Geschlechterquoten
Auf den
Schultern von
Riesen stehen.
WIE OFFEN WOLLT IHR SEIN?
WIE WOLLT IHR OFFEN SEIN?
Moment of Zen
E-Mail: Leonhard.Dobusch@uibk.ac.at
Twitter/Mastodon: @leonidobusch / @leonido@chaos.social
Web: bit.ly/LD-UIBK // dobusch.net
Research blogs:
osconjunction.net
governancexborders.com
Kontakt
Literatur
‣ Ahmed, S. (2012). On being included: Racism and diversity in institutional life. Duke University Press.
‣ Dobusch, L., Dobusch, L., & Müller-Seitz, G. (2019). Closing for the benefit of openness? The case of
Wikimedia’s open strategy process. Organization studies, 40(3), 343-370.
‣ Miles, R. E. (1965). Human Relations or Human Resources?, Harvard Business Review, July-Aug, 148–63
‣ Pirson, M., & Livne-Tarandach, R. (2020). Restoring dignity with open hiring: Greyston Bakery and the
recognition of value. Rutgers Business Review, 5(2), 236-247.
‣ Ragins, B.R./Gonzales, J.A. (2003): Understanding Diversity in Organizations: Getting a Grip on a Slippery
Construct. In: Greenberg, J. (Ed.): Organizational Behaviour and the State of the Science. Mahwah:
Lawrence Erlbaum, 125-163.
58
#1 Bedürfnis nach Teilhabe und Zugehörigkeit, ohne dass
die eigene Identität eine Rolle spielt. Man will gerade nicht
adressiert werden als „weiblich“, „schwul“, „muslimisch“ oder
„schwarz“. „Es“ soll egal sein.
#2 In der Unterschiedlichkeit gesehen werden wollen. Gerade
das Frau-, Schwul-, Muslimisch- oder Schwarzsein wird
hervorgehoben und etwa mit dem Begriff „Stolz“ (Pride) positiv
konnotiert und sichtbar gemacht.
#3 Die Unterscheidungen selbst werden dekonstruiert. Hier
werden Begriffe für nicht von Diskriminierung Betroffene
entwickelt, etwa weiß, cis, Mann. Diese letzte Position zentriert
also die Privilegien der Privilegierten und die Spielregeln selbst.
“
Kon
fl
ikte als Folge gelungener Inklusion
(El Mafaalani, 2022,
gekürzt L.D.)
https://taz.de/Der-Fall-Ferda-Ataman/!5871105/)
Source: https://commons.wikimedia.org/wiki/File:Karl_Popper.jpg
Foto: AkrimF, CC BY-SA 4.0, https://commons.wikimedia.org/wiki/File:Aladin_El-Mafaalani.jpg

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Offen. Divers. Inklusiv. Thinking the Future of Organizations

  • 1. OFFEN. DIVERS. INKLUSIV. Thinking the Future of Organizations Leonhard Dobusch Professor für Organisation Agenda UIBK Digital Leadership Club Universität Innsbruck, 21. April 2023
  • 3. First. To point out, […] the great loss which the whole country is suffering through inef fi ciency in almost all of our daily acts. Second. […] the remedy for this inef fi ciency lies in systematic management, rather than in searching for some unusual or extraordinary man. Third. To prove that the best management is a true science, resting upon clearly de fi ned laws, rules, and principles, as a foundation. “ Taylor, F. (1911). Principles of Scientific Management
  • 4. Quelle: https://youtu.be/OtoAAoTAkAM A Human Being is a Tool. It is important to measure and to record every- thing
  • 5. Bild: Álvaro Ibáñez, CC-BY 2.0, https://nl.wikipedia.org/wiki/Bestand:Amazon_Espa%C3%B1a_por_dentro_(San_Fernando_de_Henares).JPG
  • 6. ‣ „Picker“ legen Bestellungen in Trolleys ‣ Amazons Software kalkuliert den ef fi zientesten Gehweg um den Trolley zu füllen… ‣ …und instruiert die Picker von einem Regal zum nächsten. ‣ Management erhält „Echtzeit“-Informationen… ‣ …und kann Textnachrichten verschicken, die zu rascherem Arbeiten ermahnen. ‣ Eingebettet in kontinuierliche Verbesserungsprozesse (Kaizen, KVPs) Quelle: https://www.salon.com/2014/02/23/worse_than_wal_mart_amazons_sick_brutality_and_secret_history_of_ruthlessly_intimidating_workers/ Neo-Taylorismus bei Amazon:
  • 7.
  • 8. Die Zukunft von Gestern
  • 9. Source: https://hbr.org/1965/07/human-relations-or-human-resources [F]ocus attention on all organization members as reservoirs of untapped resources. “ Miles, R.E. (1965)
  • 10. Given these demographic changes, most organizations recognize that there is no turning back with respect to diversity, that diversity is a fact rather than a fad, and that companies that do a good job with diversity will gain a competitive edge over those that do not.” “ Ragins & Gonzalez (2003, p. 126) Report „Workforce 2000“ (1987)
  • 11.
  • 12.
  • 13. 13 Screenshot links aus dem Jahr 2016. Im Vergleich dazu Webseite 2020: https://www.bmwgroup.jobs/de/de/ueber-uns/diversity.html
  • 14. 14 Screenshot links aus dem Jahr 2020. Im Vergleich dazu Webseite 2023: https://www.bmwgroup.jobs/de/de/ueber-uns/diversity.html
  • 17. How smart companies are opening up strategic initiatives to involve front-line employees, experts, suppliers, customers, entrepreneurs, and even competitors. “
  • 18. Ein Beispiel für Offenheit.
  • 20.
  • 21. “Imagine a world in which every single human being can freely share in the sum of all knowledge.”
  • 22.
  • 25. The average Wikipedian on the English Wikipedia is (1) white, (2) male, (3) technically inclined, (4) formally educated, (4) an English speaker (native or non- native), (5) aged 15–49, (6) from a majority- Christian country, (7) from a developed nation, (8) from the Northern Hemisphere, and (9) likely employed as a white-collar worker or enrolled as a student rather than being employed as a blue-collar worker. “ Wikipedia:Systemic Bias, https://en.wikipedia.org/wiki/Wikipedia:Systemic_bias
  • 29. Fehlende Vielfalt trotz radikaler Offenheit?
  • 31. Non-performatives describes the “reiterative and citational practice by which discourse” does not produce “the effects that it names” (Butler 1993: 2) “ Ahmed, S. (2012, p. 117)
  • 32. The seeds of closure are always already present within the open, but the language of openness doesn’t allow us to gain any traction on that closure. “ Tkacz, N. (2020)
  • 33. The seeds of closure are always already present within the open, but the language of openness doesn’t allow us to gain any traction on that closure. “ Tkacz, N. (2020)
  • 34.
  • 35. Fehlende Vielfalt trotz radikaler Offenheit? wegen
  • 36. Bild: https://commons.wikimedia.org/wiki/File:Being_any_Gender_is_a_drag_-_World_Pride_London_2012_(7527764372).jpg,, CC BY 2.0, by xJason.Rogersx Spiegelbild gesellschaftlicher (Geschlechter-)Verhältnisse?
  • 39. 0 25 50 75 100 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 edit count in percent per year in each user group Anonymous users Bots Registered users Mehr Edits von Algorithmen (»Bots«): Aus: Müller-Birn, C./Dobusch, L./Herbsleb, J. D. (2013): Work-to-rule: the emergence of algorithmic governance in Wikipedia. Proceedings of the 6th International Conference on Communities and Technologies (C&T ’13), ACM, 80–89.
  • 40. Wenn Deine Gruppe aus neun hilfreichen und hö fl ichen Mitgliedern und einem unhö fl ichen, sexistischen und lauten Mitglied besteht, dann werden die meisten Frauen wegen dieses einen Mitglieds fernbleiben. “ Valeria Aurora (2002), http://tldp.org/ HOWTO/Encourage-Women-Linux- HOWTO/, Übersetzung L.D.
  • 42. Wikipedia- spezi fi sch Gesamt- gesellschaftlich Geschlossenheit Hackerkultur Trolle »Bots« 0 25 50 75 100 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 edit count in percent per year in each user group Anonymous users Bots Registered users Figure 3: Development of edits per user group (registered user, anonymous user, bot) in the Wikipedia’s administrative names- pace 4. continuously increasing in number. Since bots have shown their usefulness for a wide variety of tasks in the main namespace, their scope has steadily expanded, and more edits have taken place in other namespaces. This contradicts a community guideline that suggests the avoidance of editing activities of bots outside the article namespace. However, in 2012, these “outside” edits accounted for over 40 percent of all bot edits. This emergence of bot activity all over the community project is an indication of the growing importance of these “lit- tle helpers” for the community’s activities. This relates to a study that analyzed the diversification of human edits over the different namespaces. In 2001, about 90 percent of all edits were carried out in the article namespace, but in 2006, this number had already decreased to 70 percent [15]. We assume that the change in the community engagement of bot operators also expanded the reach of bot edits. More interestingly, while human edits slowed down in Wikipedia’s community space, edits carried out by bots increased as shown in Figure 3. In this administrative space, 20 different bots have been active on average (disregarding wikilink-bots). In the next part of our analysis, we specifically look at the types of activities bots carry out. Our interest is twofold: first, we classify tasks executed by bots in order to understand their relatedness to existing social governance mechanisms. Second, we examine our assumption of increasingly algorithmic rule enforcement by bots. We collected task descriptions from bots’ user pages to examine the kinds of activities in which bots are participating in the Wiki- pedia community. In single, doubtful cases we matched edits with their task descriptions to identify discrepancies and exclude those activities. Based on these data, we defined general activity types that are indicated in the first column of the table 1. These general activity types were defined in three steps. During the first round, we coded existing task descriptions collaboratively (around 100) until we had an almost stable set of activities. In the second round, we separately coded the remaining task descriptions. In the third round, we checked the assigned codes and compared them with our own decisions, and collaboratively coded all task descriptions that needed new activity types. In order to create a shared under- standing of existing activity types, the second and third rounds were an iterative process. Newly introduced activity types were always cross-validated over the whole data set. We clustered the manually defined sets of activities in activity types (cf. second column of the table 1) and identified three foci of bot activities (cf. fifth column of the table 1): (1) the content focus, (2) the task focus, and (3) the community focus. The first category contains mainly bots that are active in the article namespace. These bots are created primarily to support the curat- ing activities of their operators (for example, by using Autowiki- browser – a semi-automated MediaWiki editor13 ) or to connect dif- ferent language versions of a page through interwiki-links. The second category comprises bots that are used to support the main- tenance work of editors by compiling working lists or by informing editors about existing status changes on articles. The third category - the community focus - refers to activities that are rather unrelated to encyclopedic articles; they are more related to community rules and their enforcement. Four bots have a community focus: the CopperBot, GiftBot, Items- bot and xqbot. The CopperBot is the German equivalent to the HagermanBot of the English Wikipedia [8] that is responsible for signing unsigned comments on discussion pages. The main task of the Itemsbot was welcoming new users to the German Wikipedia by leaving a message on their personal discussion pages. Probably because of the aforementioned community consensus against bot welcome messages, the bot stopped working within two months. In 2008 and 2009, the operator of the Giftbot requested a bot flag for her bot in order to correct spelling mistakes. In both cases, the request was denied. In July 2010, the third request was successful. This time, the bot tasks included the removal of processed flagged revision requests, the dissemination of a newsletter that contains information on new edits on pages such as polls, and requests for banning users as well. All these activities were much more fo- cused on specific community needs. We assume that the operator of Giftbot learned much more about existing rules and guidelines over time and was therefore much better able to meet the needs of her fellows. The last of the four community bots is introduced in more detail in the next section. We show in an exemplary way how the activity set employed by this bot changes over time. 5.3.1 Example: xqbot In October 2008, the editor applied for a bot flag for her xqbot in order to request speedy deletions of orphan pages14 or remains of moved pages. In November 2008, the bot flag was assigned and the bot started working. Soon after this, the bot activities included over ten different tasks such as correcting double redirects, fixing links on disambiguation pages, adding missing references tags in articles, and the setting of interwiki-links. All these tasks were mainly focused on quality improvements to encyclopedic articles. In 2010, the focus changed in terms of additional tasks. This was motivated mainly by a procedural problem that occurred during an administrator re-election. In January 2010, one participant initiated a discussion by question- ing the procedure to take care of obsolete votes [31], [32]. The 13 http://en.wikipedia.org/wiki/Wikipedia:AutoWikiBrowse 14 Orphan pages on Wikipedia are articles that have no or very few incoming links. Usability Spiegelbild der Geschlechterverhältnisse Zugang zum Internet Offenheit Pfadabhängigkeiten Mögliche Gründe für Exklusion in Wikipedia
  • 43. Wikipedia- spezi fi sch Gesamt- gesellschaftlich Geschlossenheit Hackerkultur Trolle »Bots« 0 25 50 75 100 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 edit count in percent per year in each user group Anonymous users Bots Registered users Figure 3: Development of edits per user group (registered user, anonymous user, bot) in the Wikipedia’s administrative names- pace 4. continuously increasing in number. Since bots have shown their usefulness for a wide variety of tasks in the main namespace, their scope has steadily expanded, and more edits have taken place in other namespaces. This contradicts a community guideline that suggests the avoidance of editing activities of bots outside the article namespace. However, in 2012, these “outside” edits accounted for over 40 percent of all bot edits. This emergence of bot activity all over the community project is an indication of the growing importance of these “lit- tle helpers” for the community’s activities. This relates to a study that analyzed the diversification of human edits over the different namespaces. In 2001, about 90 percent of all edits were carried out in the article namespace, but in 2006, this number had already decreased to 70 percent [15]. We assume that the change in the community engagement of bot operators also expanded the reach of bot edits. More interestingly, while human edits slowed down in Wikipedia’s community space, edits carried out by bots increased as shown in Figure 3. In this administrative space, 20 different bots have been active on average (disregarding wikilink-bots). In the next part of our analysis, we specifically look at the types of activities bots carry out. Our interest is twofold: first, we classify tasks executed by bots in order to understand their relatedness to existing social governance mechanisms. Second, we examine our assumption of increasingly algorithmic rule enforcement by bots. We collected task descriptions from bots’ user pages to examine the kinds of activities in which bots are participating in the Wiki- pedia community. In single, doubtful cases we matched edits with their task descriptions to identify discrepancies and exclude those activities. Based on these data, we defined general activity types that are indicated in the first column of the table 1. These general activity types were defined in three steps. During the first round, we coded existing task descriptions collaboratively (around 100) until we had an almost stable set of activities. In the second round, we separately coded the remaining task descriptions. In the third round, we checked the assigned codes and compared them with our own decisions, and collaboratively coded all task descriptions that needed new activity types. In order to create a shared under- standing of existing activity types, the second and third rounds were an iterative process. Newly introduced activity types were always cross-validated over the whole data set. We clustered the manually defined sets of activities in activity types (cf. second column of the table 1) and identified three foci of bot activities (cf. fifth column of the table 1): (1) the content focus, (2) the task focus, and (3) the community focus. The first category contains mainly bots that are active in the article namespace. These bots are created primarily to support the curat- ing activities of their operators (for example, by using Autowiki- browser – a semi-automated MediaWiki editor13 ) or to connect dif- ferent language versions of a page through interwiki-links. The second category comprises bots that are used to support the main- tenance work of editors by compiling working lists or by informing editors about existing status changes on articles. The third category - the community focus - refers to activities that are rather unrelated to encyclopedic articles; they are more related to community rules and their enforcement. Four bots have a community focus: the CopperBot, GiftBot, Items- bot and xqbot. The CopperBot is the German equivalent to the HagermanBot of the English Wikipedia [8] that is responsible for signing unsigned comments on discussion pages. The main task of the Itemsbot was welcoming new users to the German Wikipedia by leaving a message on their personal discussion pages. Probably because of the aforementioned community consensus against bot welcome messages, the bot stopped working within two months. In 2008 and 2009, the operator of the Giftbot requested a bot flag for her bot in order to correct spelling mistakes. In both cases, the request was denied. In July 2010, the third request was successful. This time, the bot tasks included the removal of processed flagged revision requests, the dissemination of a newsletter that contains information on new edits on pages such as polls, and requests for banning users as well. All these activities were much more fo- cused on specific community needs. We assume that the operator of Giftbot learned much more about existing rules and guidelines over time and was therefore much better able to meet the needs of her fellows. The last of the four community bots is introduced in more detail in the next section. We show in an exemplary way how the activity set employed by this bot changes over time. 5.3.1 Example: xqbot In October 2008, the editor applied for a bot flag for her xqbot in order to request speedy deletions of orphan pages14 or remains of moved pages. In November 2008, the bot flag was assigned and the bot started working. Soon after this, the bot activities included over ten different tasks such as correcting double redirects, fixing links on disambiguation pages, adding missing references tags in articles, and the setting of interwiki-links. All these tasks were mainly focused on quality improvements to encyclopedic articles. In 2010, the focus changed in terms of additional tasks. This was motivated mainly by a procedural problem that occurred during an administrator re-election. In January 2010, one participant initiated a discussion by question- ing the procedure to take care of obsolete votes [31], [32]. The 13 http://en.wikipedia.org/wiki/Wikipedia:AutoWikiBrowse 14 Orphan pages on Wikipedia are articles that have no or very few incoming links. Usability Spiegelbild der Geschlechterverhältnisse Zugang zum Internet Offenheit Pfadabhängigkeiten Mögliche Gründe für Exklusion in Wikipedia EXKLUDIERENDE OFFENHEIT
  • 44. Offen für alle ist nicht offen genug.
  • 48. Offen für alle ist nicht offen genug. Corporate Edition
  • 49. M U S T E R MUSTER: Schwärzen in Lebenslauf Anonymisierung: Neutrale Stelle Lebenslauf Marianne Mustermann Persönliche Daten Marianne Mustermann Beispielallee 56 12345 Beispielshausen Tel.: 123 - 45678990 Mobil: 0123 – 456890 E-Mail: inskdjkerwurhr Geburtsdatum: 23.34.2340 Veburtsort: hseurhuserzjk Bürokauffrau Fachspezifische Erfahrungen 01.09.2007 – 31.05.2011 Assistentin der Geschäftsführung, Bereich Groß- und Einzelhandel Beispiel GmbH, Berlin Aufgaben: Vorbereitung der Buchhaltung Erstellung von Budgets Protokollführung bei Sitzungen und Besprechungen 31.05.2006 - 31.07.2007 Projektassistentin Muster AG, Berlin Aufgaben: Terminplanung und -überwachung Erstellen von Präsentationen Berufsausbildung 01.09.1977 – 15.07.1980 Muster AG, Berlin Bürokauffrau Schulbildung 1995 Georg-Büchner-Schule, Berlin Realschulabschluss Kenntnisse und Fähigkeiten Sprachkenntnisse Englisch, Grundkenntnisse EDV-Kenntnisse Gute MS-Office-Kenntnisse, sicherer Umgang mit dem Internet Sonstige Führerschein Klassen B Hobbys Schwimmen, Segeln, Volleyball Beispiel: Schwärzung von Bewerbungsbögen 49 Gleichheitsorientiert "Identity-blind"
  • 50.
  • 52. 52 Geschlechterquote tritt in Kraft Beispiel: Gesetzliche Geschlechterquoten
  • 53. 53 nigten Königreich (+16,6 Prozentpunkte) verfügen nur zwei Länder ohne Quotenregelung über einen überdurchschnittlichen Anstieg bei der Frauenquote. Von den restlichen Ländern mit Quotenregelung findet sich Spanien (+14,2 Prozentpunkte) knapp unter dem Durchschnitt. Lediglich Norwegen hatte in diesem Zeitraum nur einen Anstieg von 1,3 Prozentpunkten, jedoch trat dort die Quotenregelung schon 2006 in Kraft, 2010 war bereits ein Frauenanteil von 38,9 % erreicht. Das mit Abstand größte Minus von 2010 auf 2018 fällt dem derzeitigen EU-Ratspräsidentschaftsland Rumänien mit -10,3 Prozent- punkten zu. Veränderung des Frauenanteils in den Aufsichts- oder Verwaltungsräten Europa 2010–201846 Abbildung 2: Veränderung des Frauenanteils in den Aufsichts- und Verwaltungsräten, Angaben in Prozentpunkten47 32 32 29 22 21 18 17 17 16 16 14 10 10 10 10 9 9 9 7 7 6 6 3 3 3 2 2 1 1 1 -1 -2 -10 Quotenregelung keine Quotenregelung EU-28: +15 Quelle: Frauen.Management.Report.2019 Aufsichtsratsquote – das Jahr danach, https://www.arbeiterkammer.at/ interessenvertretung/wirtschaft/betriebswirtschaft/AK.Frauen.Management.Report.2019.pdf Beispiel: Gesetzliche Geschlechterquoten
  • 54. Auf den Schultern von Riesen stehen. WIE OFFEN WOLLT IHR SEIN? WIE WOLLT IHR OFFEN SEIN?
  • 55.
  • 57. E-Mail: Leonhard.Dobusch@uibk.ac.at Twitter/Mastodon: @leonidobusch / @leonido@chaos.social Web: bit.ly/LD-UIBK // dobusch.net Research blogs: osconjunction.net governancexborders.com Kontakt
  • 58. Literatur ‣ Ahmed, S. (2012). On being included: Racism and diversity in institutional life. Duke University Press. ‣ Dobusch, L., Dobusch, L., & Müller-Seitz, G. (2019). Closing for the benefit of openness? The case of Wikimedia’s open strategy process. Organization studies, 40(3), 343-370. ‣ Miles, R. E. (1965). Human Relations or Human Resources?, Harvard Business Review, July-Aug, 148–63 ‣ Pirson, M., & Livne-Tarandach, R. (2020). Restoring dignity with open hiring: Greyston Bakery and the recognition of value. Rutgers Business Review, 5(2), 236-247. ‣ Ragins, B.R./Gonzales, J.A. (2003): Understanding Diversity in Organizations: Getting a Grip on a Slippery Construct. In: Greenberg, J. (Ed.): Organizational Behaviour and the State of the Science. Mahwah: Lawrence Erlbaum, 125-163. 58
  • 59.
  • 60. #1 Bedürfnis nach Teilhabe und Zugehörigkeit, ohne dass die eigene Identität eine Rolle spielt. Man will gerade nicht adressiert werden als „weiblich“, „schwul“, „muslimisch“ oder „schwarz“. „Es“ soll egal sein. #2 In der Unterschiedlichkeit gesehen werden wollen. Gerade das Frau-, Schwul-, Muslimisch- oder Schwarzsein wird hervorgehoben und etwa mit dem Begriff „Stolz“ (Pride) positiv konnotiert und sichtbar gemacht. #3 Die Unterscheidungen selbst werden dekonstruiert. Hier werden Begriffe für nicht von Diskriminierung Betroffene entwickelt, etwa weiß, cis, Mann. Diese letzte Position zentriert also die Privilegien der Privilegierten und die Spielregeln selbst. “ Kon fl ikte als Folge gelungener Inklusion (El Mafaalani, 2022, gekürzt L.D.) https://taz.de/Der-Fall-Ferda-Ataman/!5871105/) Source: https://commons.wikimedia.org/wiki/File:Karl_Popper.jpg Foto: AkrimF, CC BY-SA 4.0, https://commons.wikimedia.org/wiki/File:Aladin_El-Mafaalani.jpg