Effect of Varieties of Institutional Systems on Dynamics of Explicitization
Bibliographic record
Abstract
We investigated the dynamic process of explicitization of Corporate Social Responsibility (CSR) through environmental disclosure and how this process unfolds in different varieties of institutional systems. We rely on the Varieties of Institutional Systems (VIS), a novel institutional comparative approach proposed by Fainshmidt et al. (2018), which encompasses the configurational context encapsulated by state, financial markets, human capital, social capital, and corporate governance institutions. We used a sample of 97 firms from four countries, Canada, Spain, Brazil and India, ranging from 2011 to 2018, summing up to 776 firm-year observations. We consider that the process of explicitization varies among VIS and is motivated by different institutional characteristics. We show that the explicitization process is weaker in Spain, standing for coordinated markets, while in Canada, which represents liberal markets, is significantly influenced by the extent of shareholder governance. In emerging countries under state-led configurations like India, the explicitization process is not significantly influenced by the institutional context, while Brazilian companies under family-led configurations are influenced by the capital market development and government integrity.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".