The Cost of Collaboration for Code and Art: Evidence from Remixing
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The Cost of Collaboration for Code and Art: Evidence from Remixing

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The Cost of Collaboration for Code and Art: Evidence from Remixing The Cost of Collaboration for Code and Art: Evidence from Remixing Presentation Transcript

  • The Cost of Collaboration for Code and Art Evidence from a Remixing Community Benjamin Mako Hill1,2 mako@mit.edu Andrés Monroy-Hernández2,3 amh@microsoft.com 1 Massachusetts Institute of Technology 2 Berkman Center, Harvard University 3 Microsoft Research February 26, 2013revision: b047d49 (2013/02/26)
  • Peer Production and Remixing Benkler (2006); Raymond (1999); Lessig (2008); von Hippel (2005); Giles (2005); Wilkinson and Huberman (2007); Kittur and Kraut (2008), etc. 2 / 17
  • Remixing The reworking and recombination of existing creative artifacts. Most commonly in reference to music, video, and interactive media. Widespread, and an important new communication modality (e.g., Manovich 2005; Lessig 2009) Especially among use youth (Jenkins 2006; Palfrey and Gasser 2008) 3 / 17
  • Criticism of Peer Production Others have suggested that peer production and remixing are amateurish and poor quality (e.g., Lanier, 2010; Keen, 2007). 4 / 17
  • “Could Hamlet have been written by a committee,or the Mona Lisa painted by a club? Could theNew Testament have been composed as aconference report? Creative ideas do not springfrom groups. They spring from individuals. Thedivine spark leaps from the finger of God to thefinger of Adam.” – A. Whitney Griswold (1957) 5 / 17
  • Research Questions RQ1: Are remixes, on average, higher quality than single-authored works? 6 / 17
  • Research Questions RQ1: Are remixes, on average, higher quality than single-authored works? RQ2: Are code-intensive remixes, on average, higher quality than media-intensive remixes? 6 / 17
  • Research Site A computer programming environment 7 / 17
  • Research Site A computer programming environment An online community for sharing projects 7 / 17
  • Research Site A computer programming environment An online community for sharing projects Designed and built around collaboration through remixing 7 / 17
  • Data and Measures1,271,085 projects shared before April 1, 2011. 10 / 17
  • Data and Measures1,271,085 projects shared before April 1, 2011.Outcome: Loveits 10 / 17
  • Data and Measures1,271,085 projects shared before April 1, 2011.Outcome: LoveitsIndependent Variables: Collaborativeness (Is project a remix?). Control blocks (similar to lines of code) Media elements including image and sound files. 10 / 17
  • Data and Measures1,271,085 projects shared before April 1, 2011.Outcome: LoveitsIndependent Variables: Collaborativeness (Is project a remix?). Control blocks (similar to lines of code) Media elements including image and sound files.Additional controls: Number of views Creator tenure, gender and age 10 / 17
  • Methods Negative binomial regression model on a count of loveits. loveits = β + β log blocks + β log media + βisremix + β log blocks × isremix + β log media × isremix + βage + βjoined + βfemale + β log prevloveits + β log views 11 / 17
  • Results: Plots of Prototypical Projects Blocks Media Elements 0.35 0.30 0.25Loveits De Novo 0.20 Remix 0.15 0.10 50 100 150 200 10 20 30 40 12 / 17
  • Results: Plots of Prototypical Projects Blocks Media Elements 0.35 0.30 0.25Loveits De Novo 0.20 Remix 0.15 0.10 50 100 150 200 10 20 30 40 RQ1: Remixes are rated lower. 12 / 17
  • Results: Plots of Prototypical Projects Blocks Media Elements 0.35 0.30 0.25Loveits De Novo 0.20 Remix 0.15 0.10 50 100 150 200 10 20 30 40 RQ1: Remixes are rated lower. RQ2: Code-intense remixes are rated higher. Media-intense remixes are rated lower. 12 / 17
  • Example: Code Intense Project Original Remix 13 / 17
  • Example: Code Intense Project Original Remix Remix received about the same loveits than the antecedent. 13 / 17
  • Example: Media Intense Project Original Remix 14 / 17
  • Example: Media Intense Project Original Remix Remix received less loveits than antecedent. 14 / 17
  • Limitations Ratings are a single and imperfect measure of quality 15 / 17
  • Limitations Ratings are a single and imperfect measure of quality Code & Media ≈ Functional & Artistic 15 / 17
  • Limitations Ratings are a single and imperfect measure of quality Code & Media ≈ Functional & Artistic Remixes and antecedents projects may compete for loveits 15 / 17
  • Limitations Ratings are a single and imperfect measure of quality Code & Media ≈ Functional & Artistic Remixes and antecedents projects may compete for loveits Generalizability beyond Scratch, beyond kids, beyond remixing. 15 / 17
  • Takeaways and Final Thoughts Remixes are rated lower than de novo projects by Scratch users. 16 / 17
  • Takeaways and Final Thoughts Remixes are rated lower than de novo projects by Scratch users. More code = smaller gap More media = wider gap 16 / 17
  • References Benkler, Y. (2006, May). The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press. Giles, J. (2005, December). Internet encyclopaedias go head to head. Nature 438(7070), 900–901. Griswold, A. W. (1957, June). Baccalaureate address. Keen, A. (2007, June). The Cult of the Amateur: How Today’s Internet is Killing Our Culture (3rd Printing ed.). Crown Business. Kittur, A. and R. E. Kraut (2008). Harnessing the wisdom of crowds in wikipedia: quality through coordination. San Diego, CA, USA, pp. 37–46. ACM. Lanier, J. (2010, January). You Are Not a Gadget: A Manifesto (1 ed.). Knopf. Lessig, L. (2008, October). Remix: Making Art and Commerce Thrive in the Hybrid Economy. Penguin Press HC. Raymond, E. S. (1999). The cathedral and the bazaar: Musings on Linux and open source by an accidental revolutionary. Sebastopol, CA: O’Reilly and Associates. von Hippel, E. (2005). Democratizing innovation. Cambridge, Massachusetts: The MIT Press. Wilkinson, D. M. and B. A. Huberman (2007). Cooperation and quality in wikipedia. In Proceedings of the 2007 international symposium on Wikis, Montreal, Quebec, Canada, pp. 157–164. ACM. 17 / 17
  • Example andData Appendix
  • Empirical Model loveits = β + β log blocks + β log media + βisremix + β log blocks × isremix + β log media × isremix + βage + βjoined + βfemale + β log prevloveits + β log views 19 / 17
  • Empirical Model loveits = β + β log blocks + β log media + βisremix + β log blocks × isremix + β log media × isremix + βage + βjoined + βfemale + β log prevloveits + β log views [RQ1] Are remixes, on average, rated as higher quality than de novo projects? 19 / 17
  • Empirical Model loveits = β + β log blocks + β log media + βisremix + β log blocks × isremix + β log media × isremix + βage + βjoined + βfemale + β log prevloveits + β log views [RQ1] Are remixes, on average, rated as higher quality than de novo projects? [RQ1] Are code-intensive remixes, on average, rated as higher quality than media-intensive remixes? 19 / 17
  • Regression Results (Intercept) −3.359∗ −4.828∗ log blocks 0.166∗ 0.076∗ log media 0.384∗ 0.086∗ log isremix 0.279∗ −0.091∗ female 0.000 0.284∗ age −0.006∗ −0.002∗ joined −0.020∗ −0.008∗ prevloveits 0.568∗ 0.076∗ log blocks× isremix 0.059∗ 0.021∗ log media× isremix −0.305∗ −0.103∗ views 1.226∗ N 1239470 1239470 log L -901476 -707714 ∗ ∗ indicates significance at p < .01 20 / 17