Alternative spaces of ‘work’ and inclusion for disabled people
Bibliographic record
Abstract
Western governments have emphasized paid work as a key route to social inclusion for disabled people. Although the proportion of disabled people in ‘mainstream’ employment has increased in recent decades, rates remain significantly below those for non-disabled people. Moreover, disabled workers continue to face discrimination and a lack of workplace accommodation. This paper critically examines the potential of three alternatives to the current situation. First, how might stronger ties between disabled workers and the labour movement allow for greater control over the nature of mainstream work? Second, can more enabling work opportunities be created beyond the ‘mainstream’ in social economy organizations? Finally, how might other forms of unpaid and creative work usefully challenge the dominance of paid employment as a route to social inclusion?
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.028 |
| Research integrity | 0.004 | 0.003 |
| 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".