Bibliographic record
Abstract
This chapter provides elaboration of stakeholder theory, commencing with four general perspectives on stakeholder theory as identified by Donaldson and Preston (1995). This is followed by a discussion of how CSR or corporate social responsibility has influenced thinking about stakeholders and forms an integral part of the normative perspective. Carroll’s (1993) popular CSR model has been adapted and modified for this book, providing a more integrated and relevant approach. Defining and classifying stakeholders is the third major topic covered, drawing first on generic stakeholder theory and commencing with a discussion of primary and secondary, active and passive stakeholders. Particularly attention is given to the framework provided by Mitchell, Agle and Wood (1997) that defines ‘stockholder salience’ as a combination of ‘legitimacy, power and urgency’. These terms are explored in detail. The chapter concludes with an examination of event and tourism stakeholders, including a diagram and research notes from the events and tourism literature.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".