Citizenship, globalization and the corporation
Bibliographic record
Abstract
The forces of envy, despair and terror in today's world are stronger than many of us realised. But they are not invincible. Against them, we must bring a message of solidarity, of mutual respect and, above all, of hope. Business cannot afford to be seen as the problem. It must, working with government, and with all the other actors in society, be part of the solution. UN Secretary Kofi Annan, Address to the World Economic Forum, 5 February 2002 Introduction Throughout this book we have frequently come across the phenomenon of globalization. Initially we discussed the rise of corporate participation in a citizen-like way in the governance of various global issues, such as global warming and the fight against pandemics (Chapter 2). We also saw a shifting corporate role towards a government-like involvement in, for instance, governing global markets for goods and services or governing civic entitlements in global supply chains in countries with weak governance institutions (Chapter 3). In Chapter 4 we analyzed the political aspects of the community formed by the firm and its stakeholders and frequently referred to the potential global reach of this new arena. In this chapter, we first analyze and theorize the impact of globalization on the corporate involvement in the citizenship arena more systematically and, second, examine the effects of globalization on the reconfiguration of the very notion of citizenship itself and the role of corporations in shaping, and being impacted by, this process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".