The Corporate University: An E-interview with Dave Hill, Alpesh Maisuria, Anthony\nNocella, and Michael Parenti
Bibliographic record
Abstract
Since the neo-liberal turn, corporate investment in universities has accelerated as the withdrawal of government\nfunding, among other factors, has further exposed universities to market forces. While this process offers numerous\nbenefits for corporations and wealthy individuals, it has been mostly detrimental for students, educators, and the\npublic at large. In this interview, international scholars Dave Hill, Alpesh Maisuria, Anthony Nocella, and Michael\nParenti broadly explain why corporations have been aggressively investing in universities. They address the\nnumerous ways that corporate involvement in university activity negatively impacts academic freedom, research\noutcomes, and the practice of democracy. The interview ends on a hopeful note by presenting examples of resistance\nagainst corporate influence. Their analyses focus primarily on the United States, United Kingdom, and Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".