Bibliographic record
Abstract
Since the publication of Freud's Interpretation of Dreams over a century ago, there has been a surprising dearth of systematic treatment in the psychoanalytic literature on ways to address dream interpretation in practice and in training. It is argued here that dreams remain a royal road to the unconscious and that understanding can be enriched through the application of a methodical approach which incorporates a range of different theories (despite their having generated major rifts in the history of psychoanalysis). In earlier research, I developed a four‐part sequential ‘model in theory’ for organizing, comparing and integrating contributions from different theorists to dream interpretation. The present article reports on the model's usefulness for examining case material. Analysis of three case vignettes from experienced clinicians revealed that the model's constructs hold good for examining real‐life dream material, which also suggests its applicability as a basis for training. Further work with the vignettes gave rise to a related ‘model in practice’ for understanding dream work in therapy as a triangular situation in which the dream, the patient, and the practitioner have distinct roles. Applying these models to case material, as illustrated in this paper, indicates their potential for further exploration, and to use in the structure, design and practical implementation of learning and training that specifically addresses work with dreams.
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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 | 0.095 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.010 | 0.027 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".