"Alternative Medicine for Alternative Depression": Claimsmaking, Counterknowledge, and Complementary and Alternative Medicine
Bibliographic record
Abstract
Depressives often use both CAM (complementary and alternative medicine) and conventional medicine to treat their depression. However, the use of CAM often contested as certain therapies are considered by some to be counterknowledge. Using data collected from the messages posted to three online newsgroups, I have analyzed how people use information and discursive strategies to build-up or undermine accounts justifying CAM use or non-use.Les personnes souffrant de dépression font souvent appel à la médecine complémentaire et alternative (MCA) et à la médecine conventionnelle pour traiter leur dépression. Malheureusement, certaines thérapies dites alternatives sont souvent considérés par certains comme étant de la désinformation. À l'aide de données recueillies sur trois forums de discussion en ligne, j'ai analysé comment les gens utilisent l'information et des stratégies discursives pour justifier ou réfuter les arguments visant l'utilisation ou la non-utilisation de la MCA.
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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 | 0.026 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".