Bibliographic record
Abstract
As Nobel prize-winning physicist Richard Feynman reminded us, first principle is that you must not fool yourself, and you are the easiest person to fool.1, p 12 One crucial principle in psychiatry is that all of us, no matter how intelligent or well-trained, are susceptible to being duped by specious claims. Research reveals, at best, modest and often negligible correlations between measures of intelligence and critical thinking skills, suggesting that these 2 domains are largely distinct.2 Nevertheless, because of a phenomenon known as bias blind spot, whereby most of us are keenly aware of others' mental shortcomings yet largely oblivious to our own,3 we may overestimate our capacities to distinguish dubious from well-supported psychiatric claims (for reviews of widespread biases and other errors in psychiatry, see Croskerry4 and Crumlish and Kelly5).A careful consideration of errors in thinking is germane to psychiatry and related fields because of the continuing insinuation of pseudoscientific claims into myriad domains of mental health practice.6 Pseudoscientific claims display the superficial trappings of science but lack its substance. As a consequence, they can readily fool nonspecialists-and even specialists, on occasion-into believing that they are well-supported by evidence. In contrast to developed sciences, pseudosciences tend to lack methodological and procedural safeguards against confirmation bias, the deeply entrenched tendency to seek out evidence consistent with one's hypotheses and to deny, dismiss, or distort evidence that is not.7 Such safeguards include randomization to conditions in the case of experimental designs; placebo controls; blinded designs; pre- and post-test measures with demonstrated reliability, construct validity, norms, and standardization; and rigorous peer review.8 These safeguards are far from foolproof and do not eliminate all sources of medical error.9 Nevertheless, they are widely accepted as desiderata in psychiatric research and are crucial bulwarks against commonplace errors in clinical inference.Although the boundaries separating pseudoscience from science are fuzzy,10 pseudosciences are characterized by several warning signs-fallible but useful indicators that distinguish them from most scientific disciplines. Such warning signs include an emphasis on confirmation rather than refutation of hypotheses (weighing hits more than misses), overuse of ad hoc hypotheses (after-the-fact escape hatches or loopholes) for explaining away negative findings, absence of self-correction in the face of repeated negative findings, placing the burden of proof on skeptics rather than on proponents of assertions, expansive claims that greatly outstrip the available research evidence, overreliance on anecdotal evidence (anecdata), evasion of systematic peer review, and the use of scientific-sounding but largely vacuous terminology (for example, receptors of the neuro networks with progressively lower valences11, p 318).12,13 In contrast to most accepted medical interventions, which are prescribed for a circumscribed number of conditions, many pseudoscientific techniques lack boundary conditions of application. For example, some proponents of Thought Field Therapy, an intervention that purports to correct imbalances in unobservable energy fields, using specified bodily tapping algorithms, maintain that it can be used to treat virtually any psychological condition, and that it is helpful not only for adults but also for children, dogs, and horses.14No indicator of pseudoscience should be used in isolation to disqualify a claim, because some scientific research programs make use of them as well. For example, ad hoc hypotheses play a legitimate role in science, especially when invoked judiciously. In most mature sciences, such hypotheses tend to enhance the theory's content, predictive power, or both. In contrast, in pseudosciences, ad hoc hypotheses are typically introduced as desperate measures to explain away contrary findings, and rarely enhance the theory's substance or capacity to generate successful predictions. …
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.096 | 0.046 |
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".