Justifying (Non)Discrimination Against Disabled Workers in Emerging Economies: Managerial Choice, Business Versus Moral Case Arguments and Home Versus Host Country Effects
Bibliographic record
Abstract
Abstract It is widely known that disabled people face discrimination in all walks of life, including employment. Unfortunately, legal protection often does not work as well as hoped, especially in emerging markets. This leads to the core objective of this study: to understand why firms might not discriminate against disabled people. Rather than simply identifying islands of non‐discrimination or best practice, we seek to better understand what has made them so and how much this might be replicable, taking account of legal regulation, firm policy and managerial choice. The qualitative findings reveal how non‐discrimination is underpinned by an interplay between business and moral case influences and interaction between country of domicile and origin structural effects. Building on transaction cost economics, theoretical insights are afforded on this dynamic process. Although it is often assumed that multinational enterprises infuse best practices from abroad, non‐discrimination in most instances followed country of domicile managerial choice, which in turn represented a mix of altruism and expediency. We posit that a lack of direction from headquarters might be because disability rights were assigned a somewhat low priority at central organizational level.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".