Bibliographic record
Abstract
Purpose This paper aims to describe an experiential learning activity designed to sensitize students to the prevalence and challenges of disclosing invisible disabilities in the workplace. It provides an impactful learning experience about a widespread phenomenon that receives little attention in textbooks. Design/methodology/approach The exercise assigns a hidden disability to some participants who interact with others who act as trusted friends. The interactions help participants develop their ability to interact sensitively with those who may have hidden disabilities. They explore the advantages and disadvantages of disclosing hidden disabilities at work. Guiding questions help focus deliberations during which participants consider the influence of their assigned role. Findings A plenary discussion follows where students share the outcomes of the simulation. Debriefing questions and suggested answers help instructors deepen student learning on the topic. A follow-up assignment allows participants to summarize personal learning about the subject and solidify the learning outcomes. Originality/value Most workers with nonapparent disabilities hide them, although disclosing them may help their employers provide helpful accommodations. This learning activity helps increase awareness and understanding of hidden disabilities in work settings and supports learning about disclosing and accommodating disabilities in the workplace. Instructors can use the activity to support understanding of employee rights, equity and accommodations in large or small classes, in-person or online, synchronously or asynchronously.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".