Bibliographic record
Abstract
The capability approach focuses on understanding and removing unfreedom, so it is surprising that connections between capability and oppression have been little discussed. I take seven steps towards filling that void. (1) There is an intuitive conceptual connection if we understand “oppression” as being held or confined to low capability levels. (2) Normatively, it is noteworthy that oppressed people are held at low capability levels as a result of the agency of others, even if (as in systemic or structural oppression) this effect is not always intended. (3) Capability research can contribute to explaining and understanding oppression, including systemic or structural oppression, and (4) this research not only allows but invites inquiry into what is distinctive about specific forms of oppression. (5) Why these unfreedoms are pervasive and persistent requires deeper explanations, which have agency foundations: one group contributes causally to reducing the agency freedom of others, whether this reduction is anyone’s purpose or not. (6) Our thinking about what is wrong with oppression must match our understanding of why it is pervasive and persistent; thus (7) recognising oppression as a kind of subjection is essential for understanding what is wrong with systemic oppression.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.052 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".