Uit de greep van destructieve interactiepatronen
Bibliographic record
Abstract
In any relationship, there is the chance to end up in a whirlpool that nobody wants to be in. Destructive communication patterns are often the real enemy. These patterns do not necessarily say anything about the quality of the relationship, but about how we unintentionally strengthen what we try to prevent. Canadian psychiatrist Karl Tomm developed IPscope as an act of resistance to diagnostic thinking. Where traditional diagnostics pin people to labels, IPscope focuses on relational dynamics and circular patterns. Tomm designed IPscope as an alternative to DSM diagnoses, to understand pathology relationally. Not man is the problem, but the recurring pattern. The model integrates elements from - among other things - ecological systems theory, social constructivism, social constructionism and from communicative, narrative and solution-oriented perspectives. IPscope reveals patterns in the language of those involved. By working together, clients and therapist can identify, visualise, and explore these patterns, creating new, healing patterns. This way, you no longer mop up water from the floor but plug the hole in the roof.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.047 | 0.014 |
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".