Reporting within the Corridor of Conformance: Managerial Perspectives on Work Environment Disclosures in Corporate Social Responsibility Reporting
Bibliographic record
Abstract
This paper examines managerial perspectives on work environment (WE) disclosures in corporate social responsibility (CSR) reports. WE encompasses all aspects of the design and management of the work system that affect the employees’ interactions with the workplace. The data are drawn from interviews with 20 CSR managers in large companies that are recognized as high performers in CSR. Managers reported that WE disclosures were an important part of CSR reporting and had several benefits—for example, it helped to maintain companies’ reputations as good places to work, which were of interest to both investors and potential employees. However, WE reporting was at a low level, focusing predominantly on occupational health and safety performance indicators, such as the number of employee injuries per year. We suggest that organizations derive legitimacy by reporting WE disclosures within a corridor of conformance that permits a low level of reporting and a great deal of latitude regarding what topics and how topics are disclosed, provided organizations meet institutional expectations. The corridor is maintained by an institutional environment in which, from participants’ perspectives, few external stakeholders were interested in WE disclosures representing employee well-being (e.g., psychological health) and the prevailing CSR reporting standards and guidelines provide scant information about employee health.
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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 | 0.070 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.031 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".