Conditions for Contingent Instructors Engaged in the Scholarship of Teaching and Learning
Bibliographic record
Abstract
An increasingly large number of courses in Canadian postsecondary institutions are taught by contingent instructors who hold full- or part-time positions for contractually limited time periods. Despite strong commitments to advancing teaching and learning, the labour and employment conditions for contingent instructors affect the incentives and possibilities for them to engage in the scholarship of teaching and learning (SoTL). Through a collaborative writing inquiry, the 9 authors examine the influences of three key conditions of contingency: institutional knowledge, status, and role; invisibility and isolation; and precarity. Four composite stories demonstrate the ways varied conditions of contingency may play out in contingent instructors’ lives and typically undermine the possibilities for them to pursue SoTL. Institutions present contingent instructors with a mixed message: research and SoTL are desirable and frequently encouraged, yet contingent instructors are often ineligible or hindered from engagement. Dans les établissements d’enseignement post-secondaires canadiens, un nombre de plus en plus élevé de cours sont enseignés par des instructeurs occasionnels ayant des contrats à temps plein ou à temps partiel pour des périodes contractuelles limitées. Malgré les solides engagements pour l’avancement de l’enseignement et de l’apprentissage, les conditions de travail et d’emploi des instructeurs occasionnels affectent les motivations et les possibilités qui pourraient leur permettre de s’engager dans l’avancement des connaissances en enseignement et en apprentissage (ACEA). Grâce à une enquête menée en collaboration, les 9 auteurs examinent les influences de trois conditions clés de ces emplois occasionnels : connaissance institutionnelle, statut et rôle; invisibilité et isolement; et précarité. Quatre témoignages composés montrent les manières dont les conditions variées de ces emplois occasionnels peuvent jouer un rôle dans la vie des instructeurs occasionnels et comment cela affaiblit les possibilités auxquelles ils ont accès afin de poursuivre des activités en ACEA. Les établissements présentent la situation des instructeurs occasionnels avec un message mixte : la recherche et l’ACEA sont des activités désirables et fréquemment encouragées, toutefois les instructeurs occasionnels sont souvent empêchés de s’y engager ou inéligibles.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".