Bibliographic record
Abstract
Introduction Psychiatry views humans through a spectrum of scientific specialties stretching from the “hard” natural sciences to the “soft” social sciences. But, since language is an inescapable aspect of human thought and behavior, and it is doubtful there could ever be a science of meaning, it is doubtful psychiatry can ever be completely scientific. Objectives To determine the prospects for a science of meaning, and the prospects for a science of psychiatry if we remain unable to reduce meaning to a science. Aims To consider the problem of determining what information people express or absorb via language. Methods Examine the evidence types ideally accessible to a science of meaning from a logical point of view to see how much confirmation they could provide to hypotheses concerning the information content of linguistic behaviors. Results Hypotheses about information content are not as strongly constrained by evidence as are hypotheses about physical properties. Physical properties are directly measurable, whereas information content cannot be measured, but only postulated in order to explain other behavior. Behavior, in turn, cannot be measured or detected except relative to the same explanatory system in which information content, or meaning, is embedded. Conclusions There is no absolute methodological barrier to a fully scientific psychiatry, but there are methodological problems a higher order of difficulty than in the hard sciences such as neurochemistry. Psychiatry will advance scientifically, while encountering methodological challenges with meaning that is expressive of values, desires, fears, goals, right and wrong.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".