Bibliographic record
Abstract
Why do so many otherwise intelligent patients and therapists pay considerable sums for products and therapies of alternative medicine, even though most of these either are known to be useless or dangerous or have not been subjected to rigorous scientific testing? The author proposes a number of reasons this occurs: (1) Social and cultural reasons (e.g., many citizens' inability to make an informed choice about a health care product; anti-scientific attitudes meshed with New Age mysticism; vigorous marketing and extravagant claims; dislike of the delivery of scientific biomedicine; belief in the superiority of "natural" products); (2) psychological reasons (e.g., the will to believe; logical errors of judgment; wishful thinking, and "demand characteristics"); (3) the illusion that an ineffective therapy works, when actually other factors were at work (e.g., the natural course or cyclic nature of the disease; the placebo effect; spontaneous remission; misdiagnosis). The author concludes by acknowledging that when people become sick, any promise of a cure is beguiling. But he cautions potential clients of alternative treatments to be suspicious if those treatments are not supported by reliable scientific research (criteria are listed), if the "evidence" for a treatment's worth consists of anecdotes, testimonials, or self-published literature, and if the practitioner has a pseudoscientific or conspiracy-laden approach, or promotes cures that sound "too good to be true."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".