Bibliographic record
Abstract
Since its inception Diageo has been committed to building and sustaining its reputation as a good corporate citizen. Supporting this objective is our success in the public policy arena where we work with key government and industry stakeholders on issues that influence, protect, and promote our business strategy or impact our stakeholders. Diageo (2005), 3rd Corporate Citizenship Report , 29 Introduction As we have discussed in the previous introductory chapter, three types of relationship are relevant for our analysis of corporations and citizenship. In this chapter, we turn to examining the first of these relationships, namely the possibilities and potential for, and limitations of, understanding corporations as citizens. We start with this aspect not least because the idea of ‘corporate citizenship’ has received so much attention in management theory and practice. As such, claims that corporations can be citizens or even ‘good citizens’ deserves serious examination. For many corporations, as our opening quote in this chapter suggests, it is quite natural, and indeed, reasonable to speak of themselves as good citizens. But for many commentators there are profound dangers in identifying corporations as citizens and especially in extending the entitlements of individual citizenship to such non-human, or even ‘pathological’ entities (Bakan 2004). In this chapter, we therefore ask whether we can seriously consider corporate citizens as in some way analogous to human citizens, and what the implications might be of doing so.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".