Representative‐negotiated <i>i</i><scp>‐deals</scp> for people with disabilities
Bibliographic record
Abstract
Abstract Although substantial research has been devoted to describing the challenges people with disabilities face in the workplace, much less attention has been focused on the processes that can bring about change. This article explores a proactive process, representative‐negotiated idiosyncratic work arrangements ( i‐ deals), that can create the conditions for long‐term employment for people with disabilities. Specifically, we explored the factors associated with the development and success of representative‐negotiated i‐ deals for people with disabilities. Using focus groups and interviews with employers and job developers, we identified nine factors and two prevailing conditions that explain the contexts in which representative‐negotiated i ‐deals will be successful. In doing so, we identified the negotiation stage during which these factors and prevailing conditions influence the i‐ deals negotiation process. Representative‐negotiated i‐ deals offer insights into how employees with disabilities can find more meaningful work. Together, these findings underscore how the representative i ‐deals negotiation process is only viable if the facilitating factors are present and supported, while the absence of these factors can hinder and lead to failure.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| 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".