Uncovering the Concept of Corporations’ Human Rights Performance \n\nA Quantitative Research Study of Institutional Determinants
Bibliographic record
Abstract
This study investigates the institutional determinants of western corporations’ human rights performance, specifically focused on their operations in the developing world. With privileged access to data rating corporations on human rights performance, the present study reports a statistical analysis of the influence of institutional factors while controlling for industry and firm characteristics.\nBuilding on neo-institutional theory (DiMaggio and Powell, 1983; Meyer and Rowan, 1977), this study explores whether institutions such as regulation and normative pressure influence corporations’ human rights performance. As regulation directed towards preventing human rights infringements in corporations’ overseas operations is found to be largely absent, only normative pressure is included in the statistical test. \nThe results suggest that firm size and the normative pressure for human rights in the corporations’ home country have significant influence on the community impact aspect of corporations’ human rights performance. Corporations operating in the US are found to have a lower performance compared to their counterparts in Western Europe, Australia and Canada. The “tenuous” relation to international human rights norms in the US is emphasised as central explanation for this. The control variables financial performance, risk and industry were found not to be significant predictors of the community impact aspect of corporations’ human rights performance. While the community aspect of human rights performance is the only aspect statistically explored, the findings nevertheless seem likely to hold for the supply chain management and employment practices aspects as well. Future research is needed to uncover these relationships. \nBy being the first study solely dedicated to uncovering determinants of corporations’ human rights performance, this dissertation contributes to the development of a theoretical field dedicated to uncovering determinants of corporations’ human rights performance in respect to their operations in the developing world.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".