Bibliographic record
Abstract
The False Memory Syndrome (FMS) diagnosis was very much in vogue from the late 1980s to the mid-1990s. It was an outgrowth of the belief held by many therapists that childhood sexual abuse was one of the most common causes of all forms of psychopathology. Although no longer in-vogue, the diagnosis is more recently being used for people who are falsely claiming that they were sexually abused by their priests. As was true in the earlier era, there were indeed many people who were sexually abused in childhood, but there were also many who were not and actually came to believe that they were, especially under the influence of therapists conducting "repressed memory therapy." Similarly, sexual abuse by priests has been a widespread phenomenon. Yet, there are still false accusers who are being led to believe by their overzealous therapists that they were indeed abused. As a psychoanalyst who has been asked to do forensic evaluations in many of these cases, I automatically ask myself questions about the psychodynamics of such falsely accusing patients. Here I describe those psychodynamic factors that I believe were operative in FMS cases, factors which in some cases apply to false sex abuse accusation against priests. Accordingly, this article is not simply of historical interest, but is still relevant and timely.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".