Bibliographic record
Abstract
The use of a verbal or written contract has been employed in many different modalities of treatment as a means of establishing the expectations and parameters of treatment. Traditionally, the contract is seen as serving a somewhat utilitarian purpose: setting up the practical conditions of treatment and providing a containing frame for the patient. Contracts, however, can extend to establishing agreed-upon goals of treatment, anticipating obstacles that may arise in the treatment and clarifying how they will be dealt with should they arise, and clearly defining the roles of the patient and therapist in a way that aids the exploratory process once the treatment has begun. Importantly, the mutually agreed-upon contract serves as a useful roadmap to keep the treatment focused and on track. In this article we will emphasize how the treatment contract can facilitate in-depth understanding of the patient's internal world, particularly when challenges to the contract are enacted by the patient. We will begin by briefly summarizing the functions of the contract and then focus on the key role of the treatment contract in furthering the exploratory process of the patient's dynamics as expressed in the transference/countertransference matrix.
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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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.039 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".