Bibliographic record
Abstract
Struggles for recognition are at the same time struggles over what it means to recognize and be recognized. Nancy Fraser and Axel Honneth propose two mutually exclusive ways to understand recognition: either as a matter of justice (Fraser) or as a matter of identity (Honneth). This article argues against the limitations of both of these construals of recognition, and offers a third way of construing it: as a matter of freedom. Recognition is not reducible, empirically or normatively, to any of these, however. Moreover, it needs to be regarded both more critically and more openly since what we are dealing with is a practice and an ideal that is by its very nature deeply contestable and therefore (more than most practices and ideals) subject to unforeseeable historical and normative change. Rather than trying to fix the meaning of recognition in order to give it a determinate role in ambitious theories of justice, it would be better to proceed more sceptically, attentive both to the complexities of recognition relations and to alternative ways of conceiving them and going on with them differently from before.
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 | 0.024 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.149 |
| Scholarly communication | 0.020 | 0.033 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".