Achieving Disability Inclusion: Pitfalls, Policy and Practice
Bibliographic record
Abstract
To improve the disability gap, it is crucial to address a number of different questions. What is preventing organizations from translating their inclusivity goals into action? What are the consequences of different ways of thinking or speaking about disability for disabled professionals? What challenges are people with disabilities facing at work and how can these be most effectively tackled? This symposium assembles four teams of scholars addressing these questions with qualitative, experimental and macro level data. Disability Accommodations: Disabled Prospective Employee Responses to Organizational Messaging Author: Julia A. Langdon; ESMT - European School of Management and Technology - Berlin Author: Oriane Georgeac; Boston University Author: Aneeta Rattan; London Business School Tell and Show: Disclosing an Invisible Disability at Work Author: Niki Den Nieuwenboer; University of Kansas Author: Kristina Tirol-Carmody; Indiana University Author: Linda Klebe Trevino; The Pennsylvania State University The Mind-body Divide Affects the Perceived Legitimacy of Workplace Discrimination Author: Chloe Kovacheff; University of Toronto Author: Katherine Ann DeCelles; University of Toronto Author: George Newman; University of Toronto Understanding Disability-job Fit at an Occupational Level Author: Felix Wu; Author: Victoria Toskulwong Udomsirirat; Florida International University Author: David J. G. Dwertmann; Rutgers University Author: Frederick L. Oswald; Rice University
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 | 0.157 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.027 | 0.081 |
| Scholarly communication | 0.043 | 0.051 |
| Open science | 0.008 | 0.061 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".