Teaching Cultures: Teaching Orientations, Rewards and Social-Political Influences
Bibliographic record
Abstract
For decades, scholars have studied the experiences of early childhood educators, schoolteachers, student teachers, professors, and so on. However, the experiences of teaching assistants (TAs) have largely been under-explored. By TAs, I mean graduate students who work part-time as educators, assisting undergraduate courses. In this research, I interview [N = 17] current graduate students at a university in southern Ontario, Canada, about their recent experiences working as TAs on campus. The purpose of this interviewing is to gain insight into what teaching activities TAs do, how and why, and how their broad commitments to environmental/sustainability education impact their teaching. From analyzing interview data, drawing on principles of grounded theory, I find my interview data supports, extends, and refutes how Lortie (2002) and followers (i.e., Hargreaves and Shirley, 2009) depict teaching cultures. Discussions of teaching cultures are situated in broader conversations of neoliberalism and sustainability. Research results are arranged in a didactic model, to help TAs, along with a broader audience of educational stakeholders, make more informed teaching decisions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".