Bibliographic record
Abstract
This author’s reply responds to five main issues raised by the commentators. The first two issues regard the concept of structural injustice and agents’ responsibility for it. What kind of responsibility is generated by structural injustice? How is it distinct from responsibility related to the liability of agents for interactional injustice? Addressing these issues requires clarifying how my understanding of structural injustice draws on and differs from Iris Marion Young’s account. A third issue addressed in this reply regards the question of what institutional and structural reforms or initiatives would promote emancipatory versus regressive responses to structural injustice. This question is particularly sharp in the case of redressing the injustice and alienation of settler colonial social structures on Indigenous peoples. A fourth issue relates to the question of what useful role states and international law, especially human rights law, may play in making progress towards eliminating various forms of structural injustice, such as those related to gender oppression. This response will finally address a fifth issue about reconciliation as a regulative ideal, and whether my conception of reconciliation as non-alienation of various kinds invites a tragic reading of the pursuit of reconciliation in politics.
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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.041 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".