“Nousferatu”: Are corporate consultants extracting the lifeblood from universities?
Bibliographic record
Abstract
Universities and management consultants are locked in a danse macabre. We turn to the vampire genre to elaborate on the relationship of consulting companies to the university sector, focusing on the University of Alberta in Canada and Monash University in Australia. We are academics with long experience of the consequences of change management and the employment of consultants in universities. Deb is sufficiently "long in the tooth" that her entire career spans the period of heightened government and private sector intervention in Australian universities that began in the late 1980s and more recently she has had the experience of watching this process occur again, at speed, in Canada. Ben is a representative of the National Tertiary Education Union at Australia's largest university. He is also an experienced journalist who has reported on Australian higher education and public policy for more than two decades. The essay argues that consultants and universities are engaged in a mutually dependent relationship designed to sustain each other at the expense of the public.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".