Bibliographic record
Abstract
Sicko Statistics: Michael Moore and L'E ´cole de ParisNow, if there is anything on which the biological sciences have prided themselves in these latter years it is the substitution of quantitative for qualitative formulae.If I summed up the lessons of Louis in two expressions, they would be these: Formez toujours des idees nettes.(Frame your thoughts neatly) Fuyez toujours les a peu pres.(Always avoid approximation) -Dr.Oliver Wendell Holmes, 1860 (1) America's health care system is neither healthy, caring, nor a system.-Walter Cronkite, 1993 (2) There are four times as many health care lobbyists as there are members of Congress.According to the Center for Responsive Politics (www.opensecrets.org), in 2005 there were 2,084 health care lobbyists registered with the federal government.With 535 members of Congress, that's 3.895 lobbyists per member.-SiCKO, Michael Moore, 2008 (3) THE NUMERICAL METHODIn the current debate over the future of medical care in the United States, we could do worse than to consult two authorities in the field, Prof. Pierre C. A. Louis, the father of medical statistics (4), and Michael Moore, "the angry filmmaker (5)."Moore's SiCKO suggests that we have a lot to learn from France; Prof. Louis reminds us that it wouldn't be the first time.Over a century and a half ago, Louis introduced his "numerical method" into the life sciences.He studied 77 patients with febrile pneumonia at La Pitie ´hospital in Paris and found that the number of those who improved after traditional bloodletting was no greater than of those left alone (6).He was the first to show that numbers, not intentions, determine whether medical treatment works.Moore's SiCKO showed that structure, not money, determines whether a nation's medi-Promotional poster for Michael Moore's "Sicko."Image courtesy the Weinstein Company.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.154 | 0.066 |
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".