Bibliographic record
Abstract
With the political and media spotlight falling on climate change, sustainability, the ethics of business leaders (and those in the financial services preceding the recession) as well as the other global problems in the under-developed world of poverty, HIV, etc., the business world is beginning to see the necessity of being more socially and ecologically responsible. This is not just about being ‘green’, but about exploring the full range of socially responsible behaviours. As Theodore Zeldin suggested in his book An Intimate History of Humanity: ‘The Green Movement could not become a major political force so long as it concerned itself primarily with natural resources rather than with the full range of human desires. Its setbacks are yet another example of idealism being unable to get off the ground because it has not looked broadly enough at human aspirations in their entirety’. This book, edited by Craig Smith and his colleagues, provides the research base to this growing and increasingly important field. They focus on three key issues of corporate responsibility: embedding corporate responsibility, marketing and corporate responsibility and corporate responsibility and developing countries. Their contributors are comprised of some of the leading international scholars in the field from eight different countries: Australia, Belgium, Canada, France, Italy, the Netherlands, UK and the United States. This volume is based on state of the art research, which illustrates the importance of corporate responsibility, not only in terms of the ethical and environmental challenges but also because of their business imperative.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.736 | 0.716 |
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".