The Sociology Teaching Fellowship: A Mentorship Model for Graduate Student Teacher Training
Bibliographic record
Abstract
Scholars have long emphasized the importance of teacher training in higher education, including in sociology. Such calls have led to modest improvements in opportunities for graduate students to develop teaching-related skills and experience. However, many of these opportunities are not specific to sociology and may lack a teaching component. In this paper, we outline a teaching fellowship model for graduate student teacher training that integrates group training sessions, peer collaboration, and a teaching practicum component under the guidance of a faculty mentor. In the fellowship, graduate student teaching fellows receive a stipend for sharing the development and teaching of an undergraduate course, with supports and feedback throughout. We include data from post-fellowship questionnaires and follow-up data from fellows who went on to teach their own courses to highlight the strengths of the program. The data indicate that the fellowship is an overwhelmingly positive experience for graduate students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".