Building the “Business Case” for Hiring People with Disabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it