Bibliographic record
Abstract
Back to table of contents Previous article Next article Letters to the EditorFull AccessFaulty LogicEthan Kass, D.O., M.B.A.Ethan Kass, D.O., M.B.A.Published Online:18 Aug 2006https://doi.org/10.1176/pn.41.16.0036aI find it ridiculously funny that the collective wisdom of seven psychiatrists from the United States and Canada is to stop using bags with drug company logos because it offends some people.The idea that our professional integrity and public image are somehow tarnished by carrying a bag with a Pfizer or GSK logo on, as expressed in the letter titled "Bring Your Own Bag" in the April 21 issue, is overblown. (Though I must say something does occur: At APA's 2005 annual meeting, a passerby on the street said to me, "Hey doc, can't you afford a Neiman-Marcus bag?")I imagine some people will buy into the faulty logic that because I have pens and bags with drug names on them, I'm a pawn of the pharmaceutical industry and/or endorse the escalating cost of health care. By that line of thinking, I should stop wearing my San Francisco Giants baseball cap least someone think I endorse anabolic steroid use in sports.To my well-meaning colleagues who can't bring themselves to participate in a free-market society, I suggest giving your bags imprinted with drug company logos to poor school kids. That is what I do.Coral Springs, Fla. ISSUES NewArchived
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.356 | 0.186 |
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".