Comparing groups of units through composite indicators in a non-convex approach: corporate social responsibility for the food and beverage manufacturing industry
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
Abstract This paper compares the performance of groups of units by composing indicators of corporate social responsibility (CSR) from an efficiency and productivity perspective, applicable across various industries. From a methodological perspective, our work extends the traditional input-oriented Benefit-of-the-Doubt (BoD) model in the multiplier form, by first adapting it to accommodate the non-convexities of the production set, and second, by innovatively applying it to compare indicators across groups of firms. This adaptation, pioneered in our study, leverages the framework previously established in the literature to address more complex scenarios. From an empirical perspective, we contribute by comparing the efficiency and productivity in CSR activities of food and beverage companies across regions of Europe, the United States and Canada, and Asia–Pacific over the period 2009–2018. The paper reveals that USA-Canadian firms tend to perform best with respect to CSR, followed by European firms, and that Asian-Pacific firms achieve the worst efficiency and productivity results. The study also shows that regional catching up in CSR productivity occurred over the analyzed period.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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