Bibliographic record
Abstract
The recognition of minority and minoritized groups is a central issue for contemporary societies. From the moment societies think of themselves as being composed of multiple groups, and understand themselves as being heterogenous, they must also think about the relative inclusion of these groups within their whole. The issue of recognition is at the heart of rule of law-based and openly pluralistic societies. Thinking about recognition involves not just thinking about various identities within a plural whole, but also thinking about recognition of the injuries suffered by minority or minoritized groups. Recognizing groups such as indigenous peoples or African descendants in the Americas, for example, entails grappling with a past of colonization and slavery—two situations par excellence of historical wrongs and, of course, their consequences. Hence, recognition inevitably encompasses links between identity, the experience of minoritization, the historical wrong in question, and reparations for this wrong. These links are precisely the subject of this text, and are examined in three stages: a study of conceptual approaches to recognition and reparations; a look at the narrative of a particular wrong and the demands for reparations made by a specific group, namely the black movement in Brazil, which raises the issue of slavery and its consequences; and finally an examination of the responses to such demands, in the form of laws, policies, and various types of civil society actions. The conclusion will be an opportunity to revisit the conceptual links between recognition and reparations.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.098 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".