Under the Gun of Countertransference: A Discussion of Laura D’Angelo’s “The Loaded Chamber”
Bibliographic record
Abstract
What should we do when a patient brings a loaded gun into a session? In a relational psychoanalytic paradigm, it’s impossible to know in advance what the response to a moment of such intense pressure might be. An analyst’s ability to respond quickly, in a way that facilitates change, depends on adhering to asymmetrical aspects of psychoanalytic ritual while simultaneously holding that adherence dialectically with spontaneous personal participation. Deviations from ritualized aspects of the frame in the form of spontaneous gestures are what afford improvisations their special charge; how analysts make use of their countertransference can, therefore, be the key to an enactment’s potential. This paper discusses how Laura D’Angelo’s spontaneous decision to initiate an improvisation communicated her willingness to extend herself “beyond the call of duty.” In this example, the most significant aspect of the improvisation may not be so much what was being improvised as who was initiating the improvising.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.037 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.002 | 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".