Anthropologists in/of the neoliberal academy
Bibliographic record
Abstract
This special Forum brings together short commentaries from anthropologists working in a variety of university settings and roles, to reflect on our immediate recent experiences with the imposition of public sector educational reforms. The contributions explore ongoing institutional transformations in Australia and New Zealand, Romania, Denmark, Greece, Finland, Mexico, US, Holland, Spain, Canada and the UK. We aim to establish a platform to host ongoing discussions about the changing nature of higher education and its implications for the future of anthropology. We are confident that these exchanges in Anuac will enable colleagues coping with the impacts of austerity to move together toward a coalition in favour of the university as we think it should be. Contributions of Cris Shore & Susan Wright, Vintilă Mihăilescu, Sarah F. Green, Gabriela Vargas-Cetina & Steffan Igor Ayora-Diaz, Tracey Heatherington, Dimitris Dalakoglou, Noelle Molé Liston, Susana Narotzky, Jaro Stacul, Meredith Welch-Devine, Jon P. Mitchell.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".