Learning from the Roundtables on the Sustainable Enterprise Economy: the United Nations Global Compact and the <i>next</i> ten years
Bibliographic record
Abstract
Introduction This chapter is about research that we have been conducting about the next ten years, starting from three premises. First, given the last ten years and the development of the United Nations Global Compact, what has been learned? Second, what would the world look like if the ten Principles of the Compact were implemented (and the Millennium Development Goals (MDGs) delivered)? And, third, how can we engage with a wide range of thinkers and actors around the world to see if there is commonality across cultures, industrial sectors, professions and intellectual disciplines? Our inquiry started in the House of Lords in London in January 2007, where we held six meetings of Roundtables on Sustainable Enterprise (RSE), and up to January 2009 also engaged with different participants through Roundtables in Cape Town, Toronto, New York, Sydney and Beijing as well as two international conferences at the Eden Project in Cornwall, UK, and at the Headquarters of Wessex Water in Bath, UK. If we start with the idea that the Global Compact was designed to operate as a learning system, then we can see that it is fascinating to examine it as a model of how the world might be managed this century. The state we find ourselves in as a global community, rather than an international community, is that states, companies and nongovernment and non-business organizations all have legitimacy, all have a voice, all command attention, all demand to be recognized and all claim rights and responsibilities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".