Bibliographic record
Abstract
We live in an age when matters of group identity seem particularly important. This is because recent times have seen many social and political transitions — and transition often implies the negotiation or re-negotiation of social identities. At a minimum, transitional times cast things in more obvious relief. They are almost always uncomfortable: certainly so, when destinations are unclear, and sometimes even when destinations seem desirable. And they can be much more than merely uncomfortable. Classic examples include the break-up of the Soviet Union and the subsequent problems among the former republics; new arrangements within eastern European countries, and between these countries and a western European federation that is itself experiencing many social, cultural and religious tensions; reworked perspectives on nationality and its attributes, following the events of 11 September 2001; the continuing agonies of Africa and the ripple effects of these well beyond its borders; the rise of economic powerhouses in India, Asia and South America; the rethinking of multicultural accommodations — in the traditional immigrant destinations in the new world (Canada, the United States, Australia), but also in Europe — brought about by fears of social disintegration, of balkanisation, of violent internal discord; the attention now given to, and often demanded by, the stateless populations of the world — the regional minorities of Europe, for example, but also aboriginal and indigenous peoples around the world; and so on. 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".