Cogs in the Classroom Factory: The Changing Identity of Academic Labor
Bibliographic record
Abstract
Foreword: Preserving Our Independence, Acting Together by David Montgomery Introduction: The Changing Identity of Academic Labor by Julie M. Schmid and Deborah M. Herman A New Divide: Faculty and Others Above and Below Mapping Social Positions within the Academy by Wesley Shumar and Jonathan T. Church Dueling Identities and Faculty Unions: A Canadian Case Study by Mike Burke and Joanne Naiman In a Leftover Office in Chicago by Joe Berry A New Generation, Charting New Waters More Than Academic: Labor Consciousness and the Rise of UE Local 896-COGS by Susan Roth Breitzer Pyrrhic Victory at UC Santa Barbara: The Struggle for Labor's New Identity by Richard Sullivan Unfinished Chapters: Institutional Alliances and Changing Identities in a Graduate Employee Union by James Thompson New Tactics, Old Battlegrounds Shutting Down the Academic Factory: Developing Worker Identity in Graduate Unions by Eric Dirnbach and Susan Chimonas Are You Now or Have You Ever Been an Employee?: Contesting Grad Labor in the Academy by William Vaughn The Politics of Constructing Dissent: The Rhetorical Construction of Faculty Union Membership by Darla S. Williams Afterword: Classroom, Lab, Factory Floor: Common Labor Struggles by Carl Rosen Index
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".