Bibliographic record
Abstract
Answers to Common Queries from ClientsIn this chapter we cover common queries from clients with suggested responses.1. What is meant exactly by direct information or evidence for the doubt?There usually are all kinds of triggers outside of you that may provoke an obsessional doubt.So obviously, there is information around you when you doubt.For example, you may just leave the house, see the door, and then doubt whether the door has been locked properly.However, what is meant with no direct information or evidence for the doubt is that there is no information in reality around you that supports your doubt.For example, while locking the door, you may sense or feel something out of the ordinary, like not being able to turn the key as far as usual.In that case, there is specific direct information or evidence for a doubt such as 'The door may not have been locked properly'.Obviously, this information is not conclusive, but it is sufficient to call the doubt a normal doubt.Obsessional doubts, however, occur without this type of information or evidence, and this is an important aspect of being able to tell the difference between obsessional and normal doubt.2. I never really have certainty before the doubt occurs.I doubt all the time.So where is this certainty before the doubt?There is some variation among people with OCD as to how generalized their doubt is.Some people only doubt in particular situations they encounter during the day, Clinician's Handbook for Obsessive-Compulsive Disorder: Inference-Based Therapy, First Edition.K. O'Connor and F. Aardema.
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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 | 0.013 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".