Bibliographic record
Abstract
JOHN KIRTON Can we really develop Africa, control climate change, stop nuclear proliferation and produce secure, sustainable development for all in the world?The United Nations will take its best shot in September, when the leaders of its almost 200 members assemble in New York to figure out how to meet their currently unattainable Millennium Development Goals.But their success will depend critically on the work of a smaller, more select Summit taking place sooner.On July 6-8 the leaders of the world's major democracies gather in Gleneagles, Scotland, for their annual Group of Eight (G8) meeting, together with some carefully chosen developing country guests.How Canada performs at Gleneagles on these issues matters.It could do much to determine the future of global sustainable development and Canada's influence in protecting its national interests and values.If Canada's Prime Minister wants to succeed at Gleneagles, he will have to quickly put in place bolder, better policies than the government's recent International Policy Statement proposed.The G8 has produced some striking successes since the leaders of France, the United States, Britain, Germany, Japan and Italy gathered for their first annual encounter in November 1975.Since their first appearance in 1976, Canadian leaders have made an important contribution on issues close to the Canadian soul.North-South dialogue was advanced by Pierre Trudeau as host at Montebello, Quebec, in 1981.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".