2008 APSA Teaching and Learning Track Summaries—Track Two: Graduate Education and Professional Development
Bibliographic record
Abstract
How well do we prepare our graduate students for the diverse careers they pursue in teaching, research, and outside of academia? This is the second time Graduate Education has been a track in the TLC, and this year we have also incorporated topics related to professional development. Despite the diversity of our presentations, we arrived at a unifying theme for our track: we must prepare graduate students for the multiple arenas they will enter into after graduation. We discussed at length how most of our graduate students seek something other than the traditional, research-oriented model of graduate education that we experienced. They seek a graduate experience that is civically engaged, prepares them for teaching in addition to research, and is perhaps more connected to disciplines outside of political science. Either we provide graduate students a framework of knowledge consistent with these demands or they will be left to develop these skills through trial and error alone. In support of this goal, we urge systemic change to our professional institutions that will value and reward a more holistic approach to graduate education and professional development. Elements of such change can be found in the variety of presentations contained in our track.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.111 | 0.063 |
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".