Community Identity Peer Prestige Academic Hiring in the

Community Identity: Peer Prestige & Academic Hiring in the i. Schools Andrea Wiggins, Mick Mc. Quaid, & Lada Adamic i. Conference 2008 2/28/2008

Problem Statement • i. Schools are defining an intellectual community identity as a new breed of • interdisciplinary researchers. • Members of the community must align individual identities with the i. School community identity.

Practical Problems of Identity • From 2005 i. Conference Survey: – Academic legitimacy • Organizational survival – Student recruitment – Student placement – Development of scholarly community • Publication • Funding • Interdisciplinary research

What is an i. School? • Interdisciplinary focus on information, technology and people, with diverse institutional characteristics • Common roots in computer science, library science, • information studies, and more • 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.

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 i. Schools 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 Ph. D 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 i. Schools and in Computer Science (CS) departments?

Network Data • Census of 693 identifiable full-time faculty of i. Schools with manual data collection from Internet resources in January 2007 – 674 Ph. D 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 i. School’s individual ego network into one community ego network – An ego is an i. School, for which we gathered data on faculty degrees; an alter is an institution from which i. School 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 & i. Schools Network Characteristic CS Network i. Schools Network Nodes 123 152 Egos 29 18 Alters 94 134 Edges 572 429 Average Degree 4. 7 2. 8 Total Ph. D Degrees 1121 674 Density 0. 038 0. 019 Betweenness 0. 021 0. 019 2. 2 2. 3 5 (random = 7) 4 (random = 11) 0. 23 (random = 0. 05) 0. 15 (random = 0. 08) Average Distance Diameter Clustering Coefficient

Visual Comparison

Prestige & CS Hiring • Regressed USNWR rankings on network characteristics, both node-based (eg. degree) and topologically derived (eg. Page. Rank) • CS: – Weighted Page. Rank, 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 & i. School Hiring • i. Schools: – Smaller subgroup has USNWR LIS ratings, 11 of 18 – Weighted Page. Rank, 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 i. Schools, have hired graduates of their own institution • On average, 13% of faculty in i. Schools are selfhires; 64% of those (approximately 8% overall) graduated from the program that now employs them • In most cases, self-hires from an i. School involved faculty in library science

Discussion of Self-Hiring • Several reasons for self-hiring in i. Schools – Network structure (Ph. D -> i. School) does not reflect intermediary employment – Limited availability of Ph. Ds 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 i. School – 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 i. Schools – More data, more established field • In i. Schools, 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 i. Schools’ “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 Ph. D 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 B SE B t 11. 223359 4. 294460 2. 613 * cs 0. 006258 betweenness 0. 000670 9. 340 *** cs-indegree 0. 011898 -5. 733 *** cs-weighted pagerank -0. 068210 * p <. 05, *** p <. 001 R 2 =. 8121, Adj. R 2 =. 7865, F(3, 22) = 31. 7 ***

i. School Regression Table B -0. 004923 SE B 0. 001131 t 0. 00481 ** lis-weighted 12. 604780 pagerank 2. 966607 0. 00539 ** lis-output 0. 010957 0. 00279 ** lisbetweenness 0. 053361 lis-hiring 0. 574079 0. 247805 0. 05972. entropy. p <. 1, ** p <. 01 R 2 =. 8605, Adj. R 2 =. 7675, F(4, 6) = 9. 251 ***