Bibliographic record
Abstract
r o n g Lord Armstrong's own words best introduce his memoir of the sherpa club in the 1980s: "Sylvia Ostry came into my life in the 1980s -in 1983 or 1984, I should say, but I cannot now remember in exactly which year -when she became the Canadian prime minister's personal representative for the preparation of Economic Summits of the heads of state or government of the seven major industrialised countries, or (since that was rather an indigestible mouthful) sherpa.This term (first applied in this context, I believe, by The Economist), was just coming into currency when I became the British prime minister's sherpa in October 1979.We had not then yet started to talk generally about the 'G7.'As sherpa, she was an admirable representative of her country.For myself, I am glad and proud that she became and has remained a good friend."The first Economic Summit was held in 1975 at Rambouillet in France on the initiative of the then president of the French republic, Monsieur Giscard d'Estaing, and the chancellor of the Federal Republic of Germany, Herr Helmut Schmidt.The original membership was intended to consist of the heads of state or government of France, Germany, Japan, the United Kingdom, and the United States, but before the first meeting the prime ministers of Canada and Italy were added to the group.After two years the president of the Commission of the European Community and the head of state or government of the country currently in the presidency of the Council of Ministers of the European Community (if that was not a country already represented at the summit) were invited to attend as observers.The seven member countries took turns hosting the summit.All this was of
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.014 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 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".