Bibliographic record
Abstract
I approached this assignment with trepidation.Like many people, I had always regarded statistical matters with considerable apprehension.An "artsie" in university, I gave statistics courses the widest possible berth.My tax returns always came back to me citing, as Revenue Canada politely put it, "an error on my part."Therefore, the notion of writing a book on the concept of national income -a complex statistical system -seemed daunting.The entry on national income in The New Palgrave: A Dictionary of Economics, perhaps the most authoritative font of economic information for the layman, offered little encouragement.The first sentence of its long entry on national income began with the phrase: "Although there are numerous complexities and ambiguities attached to this concept ..." A search of the library revealed that few other historians had tackled the problem.Americans like John Kendrick and Nancy and Richard Ruggles had provided their national perspective, as had Andre Vanoli in France.But on the whole, national accountants seemed to be a shy lot."All in all," Vanoli has noted, "national account compilers write little, unlike scholars and researchers whose careers often depend on publications." 1 That this book came to be written at all is firstly a testament to the convictions of the members of the National Accounts Advisory Committee, a panel of eminent economists drawn from universities, government, and private practice that connects Statistics Canada's national accounting with the society it serves.The committee's belief that Canada's national accountants had hidden their light under a bushel prompted it to seek an outside historian to write a book on Canada in the age of national accounting.Canadians, they believed, needed to be shown how the national accounts had become woven into their economic lives.I was won over by their fervour.Throughout my research and writing, the committee members were supportive in every way as
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.412 | 0.279 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".