Bibliographic record
Abstract
Over the past decade, there has been increasing interest in the concept of Corporate Social Responsibility (CSR), and the proposition that corporations should take into account the interests of stakeholders other than their shareholders. Support for this idea has come not only from corporations themselves, but from national governments, extranational organizations such as the United Nations, and non-governmental organizations. As a result, recent years have seen legislative efforts to encourage or even mandate some form of CSR, with the reporting of CSR activities recently enshrined in Danish law, and proposed legislation in Canada which seeks to regulate the activities of Canadian mining companies in developing nations. However, questions have arisen as to whether CSR advances a consistent set of interests and principles, and whether it effectively serves the societal interests it purports to advance. This paper will consider the varying definitions which have been have been advanced for CSR, and canvass the varying interests that it has been used to promote. It will identify the organizations and forces which have been termed the “drivers” of the CSR movement, and consider some of the criticisms which have been leveled against it. Finally, it considers the efforts that varying governments and international actors have taken to encourage CSR, and identifies trends which may be expected to play an increasing role in the CSR movement internationally. Corporate Social Responsibility Defined
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".