Theorizing College Governance Across Epistemic Differences: Awareness Contexts of College Administrators and Faculty
Bibliographic record
Abstract
To provide a fresh perspective on governance in Canada’s colleges, interview data from administrators and faculty were interpreted through the lens of Glaser and Strauss’ (1965) theoretical categories describing interaction between physicians and patients. An example of a “closed awareness context” is suggested around college fund-raising, while “mutual suspicion” was observed in administrator-faculty interaction around student success policy. Examples of “mutual pretense” include feigned administrator-faculty cooperation around changing college missions and faculty workload formulae. “Open awareness” or dialogue, however, occurred where professional bodies or unions intervened. Awareness contexts are central to symbolic interactionist research, which focusses on how everyday realities are constructed. Similarities between doctor-patient and administrator-faculty interactions can be seen in the examples here. For example, just as doctors feared that delivering bad news to patients might precipitate “mayhem” in the hospital, college administrators may fear that openness around divisive topics might precipitate “mayhem” in college 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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.086 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".