Bibliographic record
Abstract
Abstract This article compares two models about the nature of obsessional intrusions: one, that they are just ‘normal thoughts’ whose obsessional significance derives from their appraisal; the other, that they are specific inferences about thoughts and things, which form conditional premises and are part of the obsessional reasoning. Support for the first model comes from questionnaire studies showing that the content of obsessional intrusions is similar or even identical to intrusions in non‐obsessional people. Also there is growing clinical evidence that addressing the appraisals made about the intrusions rather than the content of the intrusions alleviates OCD symptoms. However, regarding the second model, intrusions tend to be thematic; they do explicitly take the form of an ‘inference’ (X may occur) whose development can be traced to inductive logic; ‘intrusions’ can be modified by changing inference processes, and such modification alone can reduce OCD‐related anxiety. It is proposed that both primary inference and secondary appraisal may form two separate parts of the same evaluative sequence, and both should be targeted in treatment. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".