Unknown Knowns and Known Unknowns: Framing the Role of Organizational Learning in Corporate Social Responsibility Development
Bibliographic record
Abstract
Abstract Corporate social responsibility (CSR) is now widely seen as an increasingly significant concern for firms because of moral, relational and instrumental motives. Nevertheless, practical aspects and challenges associated with CSR development in firms remains only partially understood. In this setting, the organizational learning (OL) discipline is recurrently put forward as key in the pursuit and successful development of CSR, but the existing literature remains disjointed. This study critically reviews the existing literature to conceptualize how research to date has approached CSR development in terms of OL, and to provide a two‐dimensional structuring framework of the role of OL in CSR development that emphasizes key OL‐related aspects supporting CSR development and goes beyond an organization‐centric viewpoint to consider not only learning within the organization, but also from others, and with others. In particular, the authors identify key learning processes and sub‐processes and critical areas that remain understudied. Overall, the authors propose a macro view of the work done to date at the intersection of OL and CSR, and in doing so help make the ‘OL for CSR development’ scholarship more recognizable as a sub‐discipline.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.050 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| 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".