An implicit-explicit examination of differences in CSR practices between the USA and Europe
Bibliographic record
Abstract
Purpose The purpose of this study is to validate the Matten and Moon (2008) implicit-explicit corporate social responsibility (CSR) model by examining whether the respective differences in CSR practices between Europe and the USA reflect their respective societal expectations. Design/methodology/approach The principal component analysis is used to develop an innovative societal expectations index (SEI). This study tests the relationship between SEI and CSR through panel data and t -tests. Findings The empirical findings show a significant association between the SEI and all forms of CSR, which provides empirical support for Matten’s and Moon’s implicit-explicit framework. Originality/value This study is the first to develop an SEI to validate the Matten and Moon (2008) model that predicts implicit countries would adopt and conform to broader societal expectations for CSR, and therefore be more likely to embrace CSR activities than their counterparts in explicit countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".