Bibliographic record
Abstract
Contributors to the book suggest an alternative discourse and value system to that of the market-led corporate global agenda, one that does not directly challenge corporate globalization but recognizes a parallel reality. Need and ingenuity are creating a culture that is clearly different from both North American pop culture and the high culture of the intellectual elites, and which can lead the world away from an "economics of death" to a more positive world. The New World Order does not, however, encourage naive optimism, as it recognizes that the lethal inversion of our value system, which is only beginning to be recognized, may not be acknowledged and counteracted in time to prevent disaster. Contributors include Meenakshi Bharat (University of New Delhi), James Bisset (former Canadian ambassador to Yugoslavia), Leigh S. Brownhill (OISE, University of Toronto), Keith Ellis (University of Toronto), María Figueredo (University of Toronto), Michael Mandel (Osgoode Hall Law School), John McMurtry (University of Guelph), J. Nef (University of Guelph), Jennifer Sumner (University of Guelph), Terisa E. Turner (University of Guelph), Edward Vargo (the Assumption University in Bangkok), and Gordana Yovanovich.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.012 |
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".