Accepted by the Journal of Business Ethics for the Special Issue on: Corporate Social Responsibility Implementation Implementing CSR through partnerships: understanding the selection, design and
Bibliographic record
Abstract
* The authors would like to thank the anonymous reviewers for their valuable remarks and suggestions and Ms Amelia Clarke at the Desautels Faculty of management at McGill University for her contribution and insightful remarks on the process models of collaboration. 1 Implementing CSR through partnerships: understanding the selection, design and institutionalisation of nonprofit-business partnerships Partnerships between businesses and nonprofit organisations are an increasingly prominent element of corporate social responsibility implementation. The paper is based on two in depth partnership case studies (Earthwatch-Rio Tinto and Prince’s Trust-Royal Bank of Scotland) that move beyond a simple stage model to reveal the deeper level micro-processes in the selection, design and institutionalisation of business-NGO partnerships. The suggested practice-tested model is followed by a discussion that highlights management issues within partnership implementation and a practical Partnership Test to assist managers in testing both the accountability and level of institutionalisation of the relationship in order to address any possible skill gaps. Understanding how CSR partnerships are implemented in practice contributes to the broader CSR and partnership literatures a context specific level of detail in a systematic way that allows for transferable learning in both theory and practice. KEY WORDS: Partnerships, corporate social responsibility, NGO, implementation, institutionalisation, micro-processes.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".