Review of Managed Professionals: Unionized Faculty and Restructuring Academic Labor
Bibliographic record
Abstract
state of scholarship in that domain.The first article, in particular, is very thorough in its review of several decades of literature, and impressive in the breadth of topics it tackles.It will be of great consolation to those negotiating collective agreements for professors in these times of performance indicators and accountability to learn that even teacher effectiveness researchers believe that student evaluations of instruction "should not be used indiscriminately for summative decisions about teaching effectiveness" (p.357).At the same time, it must be said that however useful these chapters are in providing an over-view of research on student evaluations of teaching, they, too, lack the critical edge and probing questions one might expect of a collection appearing in the midst of the flurry of post-modern debates about understanding, meaning and method.For those interested in the central content of this volume, I recommend the concluding chapter by Weimer.She thoroughly summarizes the essential content of eleven chapters, covering 400 pages, in less than two dozen pages.By this act, a great deal is inadvertently revealed.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".