No Rapunzel in This Ivory Tower: Finding Your Collective and Overcoming Academic Isolation
Bibliographic record
Abstract
A common refrain among graduate students and academics is that graduate school can feel isolating. For those from historically marginalized populations, the colleagues who share the closest scholarly knowledge are unlikely to also share similar experiences of academic life. This chapter provides reflections from the authors on using social media to find, create, and maintain a community and examples of how we have leveraged our community to support personal and professional growth as graduate students. In addition, we offer institutional and individual level guidance regarding how to build communities in the political science discipline and why this intentional practice of community building provides short-term and long-term benefits to graduate students, departments, and the discipline as a whole. This manuscript is part of Strategies for Navigating Graduate School and Beyond, a forthcoming volume for those interested in pursuing graduate education in political science (Fall 2022 publication)
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".