Bibliographic record
Abstract
In my response to Nils Holtug's important book The Politics of Social Cohesion, I consider its specifically political dimensions in several distinct cases, and my general argument is that Holtug is insufficiently attentive to them.Whereas Holtug's excellent book examines the philosophical justification for social justice policies, with respect to the support they offer for multicultural accommodations, my view is that more attention ought to be paid in particular to the political mobilization by minorities that has been central to securing these accommodations.Over the course of this response, I defend the view that when discussing social cohesion and its benefits, our primary focus has to be on political inclusion or political equality-indeed, political inclusion is in a profound way foundational to the inclusion that we seek in other social spaces, for two reasons: (1) it is only if political inclusion is secure that minorities can advocate for the rights they require to secure their equality, for otherwise they have to rely on the good will of others, and that good will is not always forthcoming; and (2) because, otherwise, minorities are treated as the passive recipients of majority beneficence rather than actors in their own right.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.006 | 0.009 |
| 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".