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Presentation for the 2008 iConference on hiring in the iSchool community; more focus on diversity in hiring than the prior versions.

Presentation for the 2008 iConference on hiring in the iSchool community; more focus on diversity in hiring than the prior versions.

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  • Hi, I’m Andrea Wiggins. I’m a PhD student at the iSchool at Syracuse, and the research I’m presenting today was conducted for my master’s thesis at Michigan’s iSchool.

Community Identity Community Identity Presentation Transcript

  • Community Identity: Peer Prestige & Academic Hiring in the iSchools Andrea Wiggins, Mick McQuaid, & Lada Adamic iConference 2008 2/28/2008
  • Problem Statement
    • iSchools are defining an intellectual community identity as a new breed of
    • Members of the community must align individual identities with the iSchool community identity.
    • interdisciplinary researchers.
  • Practical Problems of Identity
    • From 2005 iConference Survey:
      • Academic legitimacy
        • Organizational survival
      • Student recruitment
      • Student placement
      • Development of scholarly community
        • Publication
        • Funding
        • Interdisciplinary research
  • What is an iSchool?
    • Interdisciplinary focus on information, technology and people, with diverse institutional characteristics
    • Common roots in computer science, library science,
    • 19 schools form the I-Schools Caucus
      • Members are expected to have substantial sponsored research activity, engagement in the training of future researchers, and a commitment to progress in the information field.
    • information studies, and more
  • Survival & Emergence
    • The prevalent survival strategies for LIS schools in the 1980’s: merger with a larger partner or expansion into IT-related fields (Hildreth & Koenig, 2002)
    • Over half of the iSchools are represented as LIS school mergers or realignments
      • Merger: Rutgers, UCLA
      • Realignment: Syracuse, Pittsburgh, Drexel, Florida State, Michigan, Washington, Illinois, Indiana
  • Identity, Legitimacy & Prestige
    • Academic survival strategy to achieve organizational legitimacy and stability underlies the way an emergent intellectual enterprise develops its identity (Small, 1999)
    • Academic institutions undergoing strategic change often use prestige ratings to indirectly influence identity (Gioia & Thomas, 1996)
  • Prestige in Academic Hiring
    • Departmental prestige is shown to be an effect of the department’s position in PhD hiring networks in:
      • Management (Bedeian & Feild, 1980)
      • Finance (Bair, 2003)
      • Sociology, history & political science (Burris, 2004)
      • Sociology (Baldi, 2005)
      • Political science (Fowler et al, 2007)
  • Research Question
    • What is the relationship between peer prestige ratings and hiring network measures in iSchools and in Computer Science (CS) departments?
  • Network Data
    • Census of 693 identifiable full-time faculty of iSchools with manual data collection from Internet resources in January 2007
      • 674 PhD degrees with 100% complete data
      • Year of degree not available for other terminal degrees (MLS, JD, MD, etc.)
    • Similar data collected by Drago Radev and associates for top CS departments
    • Ranking data from US News & World Report (2006)
  • Network Construction
    • Combined each iSchool’s individual ego network into one community ego network
      • An ego is an iSchool, for which we gathered data on faculty degrees; an alter is an institution from which iSchool faculty were hired
      • Indiana’s 2 schools were merged to maintain the institution as the unit of analysis
    • Directed 2-mode network reduced to 1-mode
      • Was: School A -> Person -> School B
      • Now: School A -> School B, with edge weights
  • Comparing CS & iSchools 0.15 ( random = 0.08) 0.23 ( random = 0.05) Clustering Coefficient 4 ( random = 11) 5 ( random = 7) Diameter 2.3 2.2 Average Distance 0.019 0.021 Betweenness 0.019 0.038 Density 674 1121 Total PhD Degrees 2.8 4.7 Average Degree 429 572 Edges 134 94 Alters 18 29 Egos 152 123 Nodes iSchools Network CS Network Network Characteristic
  • Visual Comparison
  • Prestige & CS Hiring
    • Regressed USNWR rankings on network characteristics, both node-based (eg. degree) and topologically derived (eg. PageRank)
    • CS:
      • Weighted PageRank, betweenness & indegree explain 79% of the variance in USNWR ratings
      • F = 31.7, p << 0.0001 , all 3 variables reach at least p ≤ 0.01
      • Negative coefficient for indegree lowers ratings for schools with diverse hiring sources
  • Prestige & iSchool Hiring
    • iSchools:
      • Smaller subgroup has USNWR LIS ratings, 11 of 18
      • Weighted PageRank, betweenness, hiring diversity (information entropy) & output (number of graduates in the network) explain 77% of the variance in USNWR ratings ( F = 9.3, p < 0.01 )
      • Positive coefficient for hiring diversity rewards schools with faculty from a wider selection of institutions
  • Self-Hiring
    • 26 of 29 CS egos, and 17 of 18 iSchools, have hired graduates of their own institution
    • On average, 13% of faculty in iSchools are self-hires; 64% of those (approximately 8% overall) graduated from the program that now employs them
    • In most cases, self-hires from an iSchool involved faculty in library science
  • Discussion of Self-Hiring
    • Several reasons for self-hiring in iSchools
      • Network structure (PhD -> iSchool) does not reflect intermediary employment
      • Limited availability of PhDs with specific expertise; data suggest this is more often the case for LIS faculty
      • University as the unit of analysis may hide greater interdisciplinarity due to hires from other departments (e.g. PSU)
  • Faculty Areas of Study
  • Disciplinary Diversity
    • Faculty size matters
      • < 25 faculty represent 5 or fewer disciplines
      • 25+ faculty represent 8 - 12 disciplines
    • Information entropy measure of distribution of faculty areas of study for each iSchool
      • Most diverse: Michigan, Syracuse
      • Most focused: Toronto, North Carolina, Georgia Tech, UC Irvine
      • May differentiate hiring strategies that favor disciplinary diversity versus subject focus
  • Conclusions
    • Hiring network statistics reflect some aspects of peer prestige captured in USNWR rankings, more strongly in CS than iSchools
      • More data, more established field
    • In iSchools, balancing hiring from within the community and from a diversity of other sources may improve perceptions of prestige
    • Diversity in faculty pedigree may be part of the iSchools’ “special sauce”
  • Thank you!
    • Questions?
  • References
    • Bair, J. H. (2003). Hiring Practices in Finance Education . Linkages Among Top-Ranked Graduate Programs. American Journal of Economics and Sociology, 62(2), 429-433.
    • Baldi, S. (1995). Prestige Determinants of First Academic Job for New Sociology Ph.D.s 1985-1992. The Sociological Quarterly, 36(4), 777-789.
    • Bedeian, A. G. & Field , H. S. (1980). Academic Stratification in Graduate Management Programs: Departmental Prestige and Faculty Hiring Patterns. Journal of Management, 6(2), 99-115.
    • Burris, V. (2004). The Academic Caste System: Prestige Hierarchies in PhD Exchange Networks. American Sociological Review, 69(2), 239.
    • Fowler, J. H. et al (2007). Social Networks in Political Science: Hiring and Placement of Ph.D.s, 1960–2002. PS: Political Science & Politics, 40(4), 729-739.
    • Gioia, G. A. & Thomas, J. B. (1996). Identity, Image and Issue Interpretation: Sensemaking During Strategic Change in Academia . Administrative Science Quarterly, 41(3), 370 - 403.
    • Hildreth, C. R. & Koenig, M. E. D. (2002). Organizational Realignment of LIS Programs: From independent standalone units to incorporated programs. Journal of Education for Library and Information Science, 43(2), 126-133.
    • Small, M. L. (1999). Departmental Conditions and the Emergence of New Disciplines: Two cases in the legitimation of African-American studies. Theory and Society, 28(5), 559 - 607.
  • CS Regression Table * p < .05, *** p < .001 R 2 = .8121, Adj. R 2 = .7865, F (3,22) = 31.7 *** -5.733 *** 0.011898 -0.068210 cs-indegree 9.340 *** 0.000670 0. 006258 cs-betweenness 2.613 * 4.294460 11.223359 cs-weighted pagerank t SE B B
  • iSchool Regression Table 0.00279 ** 0.010957 0.053361 lis-output . p < .1, ** p < .01 R 2 = .8605, Adj. R 2 = .7675, F (4,6) = 9.251 *** 0.05972 . 0.247805 0.574079 lis-hiring entropy 0.00539 ** 2.966607 12.604780 lis-weighted pagerank 0.00481 ** 0.001131 -0.004923 lis-betweenness t SE B B