Building the “Business Case” for Hiring People with Disabilities
Bibliographic record
Abstract
This paper demonstrates a technique to empirically estimate the financial costs (or savings) of employing people with disabilities, in order to provide a mechanism for organizations to develop a “business case” for hiring these employees. We conducted a utility analysis, a technique common in Human Resources Management (HRM), to illustrate how the financial net value can be calculated based on the difference between service costs and service value. Employment costs include those related to wages, health benefits, pensions, life insurance, vacation pay, training, safety, absences, lateness, turnover, and disability accommodations. Service value estimates are based on wages and are adjusted for performance levels. The data used for our example is drawn from a food services company in Canada. Employees with disabilities in this example provided higher net value to the organization because of their average to above-average performance and lower turnover costs. More importantly, we demonstrate a process that can be used to assess the financial value of hiring workers with disabilities. Given the negative preconceptions often associated with hiring workers with disabilities, this method and example can provide evidence that will be useful for managers and disability advocates for assisting people who wish to join the workforce.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".