Can We Transcend the Next Taboo? When the Analyst Avoids the Erotic Transference and Erotic Counter-transference
Bibliographic record
Abstract
Le risque principal pour les analystes feminins dans le contexte clinique avec un patient masculin fut celui de l’evitement et de la dissociation des experiences erotiques, au risque de mener le patient vers l’abandon du traitement. Ces defenses demeurent en place chez l’analyste lorsque celle-ci se fonde a la fois sur une histoire personnelle ainsi que professionnelle et institutionnelle qui a occulte l’emploi de son transfert erotique et de son contre-transfert. En raison de la position classique de l’interpretation a titre de resistance face a tout transfert erotique, cela empeche l’analyste de demeurer dans ce transfert pour bien l’analyser et ainsi permettre de le symboliser, afin que l’analysant puisse lui donner du sens. Un exemple clinique detaille illustre ce dilemme.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".