Bibliographic record
Abstract
The purpose of this article is to speculate about the application of John Heron’s concept of Co-operative Inquiry in the context of present day western academic institutions. While this article was written in two institutional locations - one in Canada and the second in Australia, academic contexts in the rest of North America, the UK, and Australia are striking in their similarities. Given the current context of neoliberal policy enacting algorithmic governmentality in education, an application of Co-operative Inquiry as an institutional organizing framework for the work of academics in the humanities, while desirable and possible, would be highly improbable. Since protests and criticisms of the currently ubiquitous policy tend to be muted in view of the risks involved for the protesters, it is suggested that Co-operative Inquiry could be used as a subversive strategy for saving human creative capacity. Keywords: Algorithmic governmentality; corporate university; Co-operative Inquiry; Integral Model; research planning in education
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.099 |
| Scholarly communication | 0.030 | 0.040 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".