Bibliographic record
Abstract
BACKGROUND: Although the idea of occupational injustice pervades the occupational therapy literature, there has been little scholarly debate concerning this construct or the parameters of the five identified forms of occupational injustice. PURPOSE: The aims of this paper are to highlight conceptual confusions, foreground some inherent questions that have been neither acknowledged nor addressed, and question the theoretical and practical utility of five manifestations of occupational injustice. KEY ISSUES: Few theorists have contributed to the occupational injustice literature. Significant definitional confusion exists concerning the five forms of occupational injustice with some forms described as subsets of others. The inherent problems of judging occupational injustice have not been addressed. IMPLICATIONS: If occupational injustice were understood as a violation of occupational rights-human rights to achieve well-being through occupation-many of the problems of identifying a situation of occupational justice or injustice would be resolved. Using the capabilities approach to human rights would facilitate this endeavour.
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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.096 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.015 | 0.022 |
| 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".