Bibliographic record
Abstract
In most western nations, laws discourage discrimination in paid employment on the basis of disability, but for these policies to be of benefit, individuals must define their functional limitations as disabilities. There is a strong relationship between age and disability among those of working age, yet it is unclear whether older workers attribute their limitations to disability or to ‘natural ageing’. If the latter is true, they may not believe that they need or qualify for workplace accommodations (i.e. adaptations or interventions at the workplace). Similarly, if an employer ascribes a worker's limitation to ‘natural ageing’, rather than to a disability, they may not offer compensatory accommodation. Using data from the Canadian 2001 Participation and Activity Limitation Survey, this paper asks whether workers who ascribe their functional limitation to ageing are as likely as those who do not to report a need for a workplace accommodation. It also addresses whether those who identify a need for compensatory accommodations and who ascribe their limitation to ageing have unmet workplace-accommodation needs. The findings suggest that, even when other factors are controlled, e.g. the type and severity of disability, the number of limiting conditions, gender, age, education, income and occupation, those who made the ageing attribution were less likely to recognise the need for an accommodation; and among those who acknowledged a need, those who ascribed their disability to ageing were less likely to have their needs met.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".