Geoscience academic hiring networks reinforce historic patterns of inequity
Bibliographic record
Abstract
Abstract An analysis of the academic hiring networks in geoscience reveals a severe imbalance that favors graduates from a small handful of institutions. In this study, social network analysis was conducted on a database consisting of every individual with a Ph.D. working in a geoscience degree-granting program in the United States (n = 6694) between 2015 and 2021. Individuals were mapped from the institution where they earned their Ph.D. to the institution where they currently work. Of the 895 geoscience degree-granting institutions included in the database, 10 alone produced nearly a quarter (24.6%) of the entire academic geoscience workforce. Network analysis also identified a small, closed network consisting of five of the top-10 institutions, which suggests that these networks hire more frequently from one another than from other institutions in the network. When academic rank was used to analyze the network for change over time, no significant shift in the hiring patterns was found. These imbalances in faculty production disadvantage scientists who are educated at programs other than the top-placing institutions and ultimately reinforces longstanding inequities in the field, such as the underrepresentation of people who are Black, Indigenous, People of Color (BIPOC), and first-generation college students in geoscience faculty. These patterns of inequity have also been shown to limit the spread of new scientific ideas throughout research communities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".