The End(s) of History: Questioning the Stakes of Historical Reason
Bibliographic record
Abstract
The essays featured in this special issue stem from two workshops that took place in Montreal at Concordia University in 2011 and in Vancouver at the University of British Columbia in 2012. By entitling the workshops “The End(s) of History: Questioning the Stakes of Historical Reason,” we adopted a “big picture” thematic that we hoped would appeal to a diverse set of scholars working across sub-disciplines in law, the social sciences, and the humanities. The responses that we received exceeded our most ambitious expectations both in terms of their quality and indeed their number. Some of the essays that came from these workshops have been published as an anthology by Routledge Press, and now with this special issue, the Journal of Historical Sociology has provided us with a platform to present the complete set. We are grateful to SSHRC, and to Concordia University and the Faculty of Law at the University of British Columbia for hosting our events. In order to provide the reader with the context of these essays I have taken the liberty of placing the original call for papers below. ***** I call disaster that which does not have the ultimate for a limit: it bears the ultimate away in the disaster.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.068 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".