Hiding and Seeking: A Heuristic Self-Inquiry into Concealment Discovery and Peek-a-Boo
Bibliographic record
Abstract
Early childhood games of concealment, such as peek-a-boo and hide and seek, have featured in psychoanalytic literature since the 1920’s. Sigmund Freud identifyed the symbolic nature of these games by postulating that they allow the child to experience the absence or presence of the loved object, predominantly the mother or primary caregiver. Although strongly rooted in Western culture, concealment games such as peek-a-boo are recognised within diverse cultures around the world. In psychoanalytic literature there is an emphasis on the role of mutual gaze, mirroring, containment and how games like peek-a-boo strengthen the infant’s social relationships and ego development within the first year and a half of life, as well as the resulting pathology in its absence. The aim of this research is to explore how the game of peek-a-boo presents beyond the developmental expectancy of early childhood, physically and emotionally, by examining the subjective experience of connection and disconnection, past and present through the eyes of a Child and Adolescent Psychotherapist. The question asked is this: What is the psychotherapists’ experience of peek-a-boo? Exploring physical and emotional concealment and how this may limit or benefit self-expression, identity and creativity, both personally and within a psychotherapeutic context is at the heart of this research. An interpretivist approach drawing on the heuristic methodology of self-inquiry has been employed to delve into the theme of visual retreat experienced throughout the authors life.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".