Model of Successful Corporate Culture Change Integrating Employees with Disabilities
Bibliographic record
Abstract
Abstract Purpose The purpose of this chapter is to survey and synthesis the literature on: (1) myths and misinformation about persons with disabilities that create attitudinal barriers to employment, (2) best practices in employing persons with disabilities, (3) the business case for hiring persons with disabilities and (4) corporate social responsibility and disability, in order to distill a model for changing corporate culture for successfully integrating employees with disabilities into an organizations workforce. Methodology/approach An extensive review of the above mentioned literature is synthesized and distilled into a model. Findings The review indicates a number of best practices to be implemented in order to successfully integrate employees with disabilities into the workforce. These factors have been synthesized into a model to guide employers in affecting corporate cultural change to address the integration of person with disabilities into the organization. Practical implications A systematic approach to integration of employees with disabilities, informed by the significant business logic for doing so. Originality/value The chapter provides an extensive survey of the literature on disability employment and highlights attitudinal barriers to employing persons with disabilities, the business case and social responsibility case for employing persons with disabilities, the best practices for success and synthesizes these factors into an original model to guide business in cultural change making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".