Child analysis in a changing world. In the face of a paradigm shift from the mind to the brain: Can we meet the challenge?
Bibliographic record
Abstract
Child analysts and child therapists are now practicing in a new environment where an increased emphasis is made on identifying the symptoms and finding the shortest possible way to get rid of them, from the knowledge of brain functioning. From that perspective, the history of the symptom, of the child who owns it, and of the family in which this child is being raised are not emphasized as strongly. Searching for a specific meaning of such symptoms seems to be of no more interest. How can such a heavy trend be met? Major changes also have occurred within the psychoanalytic milieu. Advances from the observational world, from Spitz (1945) to Bowlby's (1987) attachment theory, provide essential knowledge to the field of early child-parent interactions. Longitudinal research in recent decades has shown close ties between early development and future outcomes. Such new knowledge is inspiring child analysis with very young children as well as with severely disturbed, older children. Early intervention with disadvantaged populations is showing the importance of the nonspecific aspects of the therapeutic relationship as a factor of change. In light of recent neurobiologic research on the influence of a specific environment-attachment experiences-on early development of the brain, it is now possible to speak of the social brain (Cozolino, 2006).
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".