Reality versus fantasy: Reply to Lynn et al. (2014).
Bibliographic record
Abstract
We respond to Lynn et al.'s (2014) comments on our review (Dalenberg et al., 2012) demonstrating the superiority of the trauma model (TM) over the fantasy model (FM) in explaining the trauma-dissociation relationship. Lynn et al. conceded that our meta-analytic results support the TM hypothesis that trauma exposure is a causal risk factor for the development of dissociation. Although Lynn et al. suggested that our meta-analyses were selective, we respond that each omitted study failed to meet inclusion criteria; our meta-analyses thus reflect a balanced view of the predominant trauma-dissociation findings. In contrast, Lynn et al. were hypercritical of studies that supported the TM while ignoring methodological problems in studies presented as supportive of the FM. We clarify Lynn et al.'s misunderstandings of the TM and demonstrate consistent superiority in prediction of time course of dissociative symptoms, response to psychotherapy of dissociative patients, and pattern of relationships of trauma to dissociation. We defend our decision not to include studies using the Dissociative Experiences Scale-Comparison, a rarely used revision of the Dissociative Experiences Scale that shares less than 10% of the variance with the original scale. We highlight several areas of agreement: (a) Trauma plays a complex role in dissociation, involving indirect and direct paths; (b) dissociation-suggestibility relationships are small; and (c) controls and measurement issues should be addressed in future suggestibility and dissociation research. Considering the lack of evidence that dissociative individuals simply fantasize trauma, future researchers should examine more complex models of trauma and valid measures of dissociation.
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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 | 0.028 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.062 | 0.078 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".