Bibliographic record
Abstract
Abstract In most developed countries, disability income support caseloads are on the rise. Little empirical knowledge exists, however, about how policy‐makers design these programmes, contributing to caseload growth. This article specifically explores how the boundary between who is eligible and who is not for disability income support is drawn in Australia and Canada. Forty‐five interviews were conducted between March 2012 and September 2013 with informants who were or are currently involved in designing disability income support in these jurisdictions. Analysis followed the fundamental steps of grounded theory. Findings revealed that the informants describe this process as “gatekeeping,” which can be subdivided into two stages: (a) establishing the gate (definition of disability) and (b) operating the gate (who interprets the definition and how). I present the results using a conceptual model I developed, deconstructing each stage of the process of gatekeeping into discrete units of analysis. The model is useful for future comparative studies, providing a historical perspective and allows policy researchers to concentrate on specific aspects of the process in detail, which could lead to finding solutions to the challenges related to disability income support.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".