Advancing Research on Workplace Experiences of Persons with a Disability
Bibliographic record
Abstract
This presenter symposium features five research studies by well-known disability scholars, from North America and Europe, on the inclusion and workplace experiences of employees with disabilities. Together, these studies extend the boundaries of disability research by showcasing inadvertent ways in which new supposedly inclusive workplace designs (e.g., alternative/digitalized work/conference spaces) can become empty promises or perpetuate social inequality; how and why career advancement may be stalled for some employees with a disability; how an employee’s identity can be associated with their disability management strategy, and in turn their help-seeking preferences; and finally how even conventional large organizations may unintentionally exclude employees with a disability. Our discussants will synthesize the individual presentations to suggest future research directions as well as practical suggestions for promoting equity and inclusion within organizations as well as within important spaces of organizing such as this Academy of Management annual meeting. (In)visibilizing Disability in Activity-Based Working Author: Ive D. Klinksiek; UCLouvain Author: Eline Jammaers; Hasselt U. Author: Laurent Taskin; Critical Management Studies COVID-19 and Conference Accessibility for Persons with Disabilities Author: Joy E. Beatty; Eastern Michigan U. The Effect of Stereotype-Inconsistent Behaviour on the Feedback Provided to Employees Author: Catherine Connelly; McMaster U. Author: Silvia Bonaccio; Telfer School of Management, U. of Ottawa Author: Ian R. Gellatly; U. of Alberta Author: Sarah-Kay Walker; McMaster U. Author: Jennifer Ho; DeGroote School of Business, McMaster U. Differential Help-Seeking Patterns among the Invisibly Disabled Author: Jordan Nielsen; Purdue U. Author: Seonyoung Ji; Purdue U., West Lafayette Author: John Lynch; U. of Illinois at Chicago Author: Julia Stevenson-Street; Purdue U., West Lafayette Experiences of State Agency Employees with Disabilities who Request Accommodations Author: Christine Nittrouer; Texas Tech U. Author: Claudia Cogliser; Texas Tech U.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".