“I feel like I'm changing people's lives, even if it's just two hours at a time”: Understanding contingent instructors’ emotion management in university teaching
Bibliographic record
Abstract
This article extends existing scholarship on contingent or temporary-contract university instructors' emotional agency by employing the Bolton's emotion management and Cottingham's emotional capital typologies in tandem. In interviews with 40 instructors from universities across Canada, participants described acquiring both primary and secondary emotional capital as an embodied psychosocial resource through past education, upbringing and culture, and knowledge and skills from previous work and training experiences respectively. They then deployed this capital through emotion management based in both social and organizational feeling rules in their capacity as professors. This allowed instructors to reinforce their own sense of purpose, authority and competence as instructors, and to establish fulfilling relationships with students through teaching and mentoring which they infused with personal meaning. However, instructors' agency was also curtailed to varying degrees, by both institutional attitudes around academic contingency and sexist, and in some cases, racist or otherwise patronizing attitudes from students. Despite this, instructors were often able to reaffirm their identities as instructors by using emotion management in self-affirming ways, such as by drawing on self-confidence gained through previous occupations and training, and facilitating cultural backgrounds shared with students through emotional management.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".