MRI Ideas as a Compass in the Dark: A Beginning Therapist Applies MRI Ideas to Dangerous Situations
Bibliographic record
Abstract
The foundational ideas of the Mental Research Institute (MRI) can offer grounding to a therapist when working with dangerous or emotionally fraught situations. In this article, a beginning therapist discusses how these foundational ideas helped her overcome initial biases to work successfully with potentially dangerous court-mandated clients; helped her to handle an emotionally fraught situation in her own family; and clarified her work with a client in a potential domestic violence situation, which might have required reporting to child welfare authorities. Key MRI concepts including the theory of groups; the theory of logical types; first and second order change, cybernetics and positive and negative feedback; context-maintaining behaviors; attempted solutions which become problematic; and therapist maneuverability are discussed. Basic MRI interventions are defined and discussed, including but not limited to the go-slow directive, the dangers of improvement, making a “U-turn,” and how to worsen the problem. A case study is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".