Accuracy of Defense Interpretation in Three Character Types
Bibliographic record
Abstract
Defense mechanisms are one of the most durable constructs in psychoanalysis and dynamic psychiatry/psychology, spanning theoretical, clinical, and research approaches. While the construct originated with Freud’s 1894 [1] publication, The Neuro-Psychoses of Defence , the first seven decades of psychoanalytic writing largely advanced the theoretical understanding and clinical approaches to defense mechanisms, while the research did not begin in earnest until about the last 40 years, accelerating somewhat more recently. Much of this research has understandably concentrated first on issues of how to assess defenses [2, 3], second, on the relationship of defenses to clinical disorders, such as depression [4] and personality disorders [5, 6], and, third, on change in defenses over time and long-term development [7]. In recent years, this latter avenue has expanded to include treatment outcome studies indicating that defenses and defensive functioning improve with treatment [4, 8–10]. To date, these have been naturalistic observational studies of patients in treatment and follow-up, but they have also begun to examine the role of defenses in the processes of change with psychotherapy. Kramer et al. [11] found that change in distress was mediated by prior improvement during psychotherapy of defensive functioning, but not of conscious coping. Perry and Bond [12] reported that change in defense mechanisms at 2.5 years of long-term dynamic psychotherapy predicted change in multiple measures of symptoms and functioning at 5 years. While we await additional research to establish that change in defenses mediates improvement in symptoms and functioning, it is important to explore and delineate therapeutic processes that lead to change in defenses. This chapter, then, is an effort to examine some early hypotheses and approaches to determining how therapeutic interventions lead to change in defensive functioning within and across psychotherapy sessions.
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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 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".