Bibliographic record
Abstract
I f Michel Foucault is right that war is now “the motor behind institutions and order” and “a battlefront runs through the whole of society, continuously and permanently,” then we must try to understand what forces generate this permanent state of war and which side of the battle we want to be on, because, as Foucault insisted, “There is no such thing as a neutral subject.” If we are to heed Foucault’s warning that “We are all inevitably someone’s adversary,”l then we must make our decisions carefully based on an understanding of what kind of world we are currently living in and what kind of world we want to pass on to future generations of young people. Indeed, the war we are faced with today is global in scope as the forces of neoliberalism are on the march, dismantling the historically guaranteed social provisions provided by the welfare state, defining profitmaking as the essence of democracy, imposing rapacious free-trade agreements, saturating noneconomic spheres with market rationalities, and equating freedom with the unrestricted ability of markets to “govern economic relations free of government regulation.” 2 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.031 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".