Intellectual Lineages D ANIEL A. MC F ARLAND S ANNE S MITH D AVID J URGENS
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1 Intellectual Lineages D ANIEL A. MC F ARLAND S ANNE S MITH D AVID J URGENS
2 Why Study Lineages?!! Faculty are key players in the scientific workforce!! Their research agendas shape discovery!! Faculty have long careers and train many descendants!! Point: the system of faculty production is key to understanding the long-term spread of knowledge!!! Our focus: reproductive success of scholars
3 RESEARCH QUESTIONS!! Reproductive success!! Which PhD s become faculty advisors?!! Which PhD s go on to have the largest intellectual lineages?!! Is there inequality?!! Field!! STEM vs Non-STEM!! Disciplinarity vs interdisciplinarity!! Institution!! Elite vs non-elite!! Private vs public!! Persons!! Male vs female
4 Collect Lots of Data! Focal datasets ProQuest dissertations (still scraping) # theses = 1,037,540 # departments = 12,869 # universities = NRC data on ranking Top 10, 25, 50, rest US NEWS for professions Example data University* University*of*Chicago* Year* 1999* Name** Daniel*A.*McFarland* Subject* Sociology,*EducaDon* Advisor* John*PadgeG* Title* EducaDonal*Social*Systems*and* their*organizadon* University* Stanford*University* Year* 2007* Name** Sarah*Ramage* Subject* Sociology,*Computer*Science* Advisor* Daniel*McFarland* Title* Mapping*Knowledge*Domains*in* TwiGer*and*Academe* University* Carnegie*Mellon*University* Year* 2015* Name** Jenny*Doe* Subject* Computer*Science* Advisor* Sarah*Ramage* Title* New*Forms*of*Latent*Dirichlet* AllocaDon*
5 Data Comparison with NCES (!!!!!" '!!!!!"!""#$%&'&'#())(*()+)#,-+.#/01&234&5# &!!!!!" %!!!!!" Doubled in 30 years! $!!!!!" #!!!!!"!" )*+,-./0" 1234"
6 #!!!!" '#!!!" '!!!!" &#!!!" &!!!!" %#!!!" %!!!!" $#!!!" $!!!!" #!!!"!"!"#$%&'()*'+&,-",.%/' Doubled in 30 years! $()!" $()%" $()'" $()*" $())" $((!" $((%" $(('" $((*" $(()" %!!!" %!!%" %!!'" %!!*" %!!)" %!$!"
7 *+,%&-)$)$%./%!0)1% #!!!!" '#!!!"!"#$%&#'()% '!!!!" &#!!!" &!!!!" %#!!!" %!!!!" $#!!!" $!!!!" #!!!" +,-./0"/12"345/6.,7/0" " A1B B" 3.,0,B.-/0"/12"?4/0<5" " $()!" $()%" $()'" $()*" $())" $((!" $((%" $(('" $((*" $(()" %!!!" %!!%" %!!'" %!!*" %!!)" %!$!"
8 !"#$%&'('($)*$+',-'.$ #!!!!" '#!!!" '!!!!" &#!!!" &!!!!" %#!!!" %!!!!" $#!!!" $!!!!" #!!!"!" $()!" $()%" $()'" $()*" $())" $((!" $((%" $(('" $((*" $(()" %!!!" %!!%" %!!'" %!!*" %!!)" %!$!" +,-./," 0./," "
9 *+,%&-)$)$%./%0123% '!!!!" &#!!!" &!!!!"!"#$%&#'()% %#!!!" %!!!!" $#!!!",-./" 012"#!" 012"%#"" 012"$!" $!!!!" #!!!"!" $()!" $()$" $()%" $()&" $()'" $()#" $()*" $()+" $())" $()(" $((!" $(($" $((%" $((&" $(('" $((#" $((*" $((+" $(()" $(((" %!!!" %!!$" %!!%" %!!&" %!!'" %!!#" %!!*" %!!+" %!!)" %!!(" %!$!"
10 How do we capture reproductive success? 1. Reproduction likelihood (% chance of advising) Avg = 5% overall (stdev 20%) 2. Time to reproduction (years to first primary advisee) Avg = 10 (stdev 5) 3. Size of lineage (# descendants [primary advisee]) Avg = 4.8 descendants (stdev 6.2) Implication: long term knowledge growth most likely will go in these directions
11 Program-Rank View Key: X = time to reproduction, Y = reproduction likelihood, size = size of lineage, color = NRC ranks
12 Field View Key: X = time to reproduction, Y = reproduction likelihood, size = size of lineage, color = field Engineering Biology & Health
13 Huge institutional inequality in hiring Fraction of institutions institution 1st job institution 2nd job+ Gini coefficient.713, % of universities account for 60% of the PhD s Fraction of jobs p
14 Distribution of 1 st Hire by Institutional NRC Rank Ignore bottom includes nonranked (bus, educ, law)
15 Job iversity Flows Across Institutions City University of New York New York University Duke University Boston University Columbia University University of of Oregon California Santa BarbaraThe Johns Hopkins University University of Pennsylvania Rutgers The University The State of University Chicago of New Jersey New Brunswick The University of North Carolina at Princeton Chapel Hill Yale University University University of California San Diego Harvard University Massachusetts Institute of Technology California Institute of Technology Georgia Institute of Technology Carnegie Mellon University University of Maryland College Park Stanford University University of California Berkeley University of Colorado at Boulder The Pennsylvania State University University of California Los Angeles The University of Wisconsin Madison University of Southern California Purdue University Northwestern University University of Michigan Michigan State University University of Washington University of California Irvine The University of Texas at Austin University of Illinois at Urbana Champaign University of California Davis The Ohio State University University of Cornell Minnesota The University of Iowa The University of Arizona Iowa State University Texas A&M University University of Georgia University of Kentucky Indiana University State University of New York at Buffalo University of Missouri Columbia Wayne State University Arizona State University University of Florida North Carolina State University
16 What corresponds with reproductive success? Descriptive variables 1. Female vs male graduate 2. Public vs private university 3. Program rank (NRC 2011) top 10, 25, 50, rest 4. STEM vs Non-STEM program 5. Interdisciplinary thesis vs disciplinary thesis 6. Control: Supply and demand (# advisees and faculty)
17 Initial Predictive Models (1) Logistic regression model for reproduction (1/0) -Correction for multi-way clustering on field and institution -Fixed effect for years (2) Negative binomial regression model for reproduction (> 0) -Correction for multi-way clustering on field and institution -Fixed effect for years
18 Odds ratios for reproducing (0/1) stem male isciplinary top 50 top 25 top 10 private
19 Odds ratios for reproducing (>0) stem male isciplinary top 50 top 25 top 10 private
20 Longitudinal Patterns How do these effects change with age? (years since phd) How do these effects differ by cohort? (when phd graduates) How do these effects differ by period? (when phd graduates an advisee)
21 AGE EFFECTS Birth rate by age and stem (incl. barren PhDs) % reproductions (n births / n possible 'mothers') Stable! non-stem stem Age
22 COHORT EFFECTS Birth rate by cohort and stem (incl. barren PhDs) % reproductions (n births / n possible 'mothers') non-stem stem Diminishes! Cohort
23 PERIOD EFFECTS Birth rate by period and stem (incl. barren PhDs) % reproductions (n births / n possible 'mothers') non-stem stem Period (birth advisee)
24 AGE EFFECTS Birth rate by age and gender (incl. barren PhDs) % reproductions (n births / n possible 'mothers') Gender unknown male female Age
25 COHORT EFFECTS Birth rate by cohort and gender (incl. barren PhDs) % reproductions (n births / n possible 'mothers') Gender unknown male female Cohort
26 PERIOD EFFECTS Birth rate by period and gender (incl. barren PhDs) % reproductions (n births / n possible mothers) Gender unknown male female Period (birth advisee)
27 AGE EFFECTS Birth rate by age and interdisciplinary thesis (incl. barren PhDs) % reproductions (n births / n possible 'mothers') Tenure affordance? Thesis is not interdiscplinary interdisciplinary Age
28 COHORT EFFECTS Birth rate by cohort and interdisciplinary thesis (incl. barren PhDs) % reproductions (n births / n possible 'mothers') unpopular! More popular! Thesis is not interdisciplinary interdisciplinary Cohort
29 PERIOD EFFECTS Birth rate by period and interdisciplinary thesis (incl. barren PhDs) % reproductions (n births / n possible 'mothers') Clear period effect! Institutionalization? Thesis is not interdisciplinary interdisciplinary Period (birth advisee)
30 AGE EFFECTS Birth rate by age and rank (incl. barren PhDs) % reproductions (n births / n possible 'mothers') rank NCR / World News 1st rank 2nd rank 3rd rank 4th rank Age
31 COHORT EFFECTS Birth rate by cohort and rank (incl. barren PhDs) % reproductions (n births / n possible 'mothers') rank NCR / World News 1st rank 2nd rank 3rd rank 4th rank Cohort
32 PERIOD EFFECTS Birth rate by period and rank (incl. barren PhDs) % reproductions (n births / n possible 'mothers') rank NCR / World News 1st rank 2nd rank 3rd rank 4th rank Period (birth advisee)
33 Summary Clear Inequities STEM > Non-STEM Cohort and period effects (later > earlier) Male > Female Stable with mild suggestion of diminishing Interdisciplinarity mixed Age, Cohort and Period Effects (mid-career, later cohorts, late demand) Elite (top 10) > Rest Stable! Same for private > public
34 Future Steps 1. Multilevel cohort-period-age analysis a. Multivariate model b. Find an instrumental variable c. Identify mechanisms of change 2. Improve data a. Include productivity b. Include funding c. Include NCES survey data
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