Dissociation : Vol. 4, No. 4, p. 205-213 : The dissociative table technique: a strategy for working with ego states in dissociative disorders and ego-state therapy
Bibliographic record
Abstract
There are various ways to contact alter personalities (ego states) in Multiple Personality Disorder (MPD) and other dissociative disorders.This paperpresents one such strategy that the author has developed over the past decade working with such patients.Keeping in mind that therapists must constantly be on guard against the iatrogenic creation of alter personalities, there are nonetheless ways in which the inner ego states previously formed and already operating in the patient s life can be learned prior to any therapeutic intervention.This assures that the search for these inner states is lead by cues from the patient and not from the therapist.This writing will outline an inter-related series oftechniques which should prove helpful to those seeking a strategy to access the inner ego system ofthose sufferingfrom disorders ofdissociation.Not only is this a technique Jor accessing alter personalities, but it also offers additional strategies to assist the work with these alters throughout the course oftherapy.It is not a therapy in itself, but rather a group of adjunctive strategies to be used in conjunction with the clinical approach ofthe therapist who may wish to use this technique.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".