Meaning-making across disparate realities: A new cognitive model for the personality-integrating response to fairy tales
Bibliographic record
Abstract
Abstract This paper reviews the extant literature on ways readers make meaning from fairy tales, and proposes a new cognitive model for the response to the traditional fairy tale. Much of the available research on literary responses to fairy tales comes from within the boundaries of psychoanalysis (Bettelheim 1975.The uses of enchantment: The meaning and importance of fairy tales. New York: Vintage; Dieckmann 1986.Twice-told tales: The psychological use of fairy tales. Wilmette: Chiron; Miller 1984.Thou shalt not be aware: Society’s betrayal of the child. New York: Farrar, Straus, Giroux), as well as folklore studies and anthropology (Zipes 1983.Fairy tales and the art of subversion: The classical genre for children and the process of civilization. New York: Wildman Press; Zipes 2006.Why fairy tales stick: The evolution and relevance of a genre. New York: Routledge; Zipes 2012.The irresistible fairy tale: The cultural and social history of a genre. Princeton: Princeton University Press.). Although these fields do not often overlap, the therapeutic potential of fairy tales and relatively recent popularization of practices such as bibliotherapy (Jack and Ronan 2008. Bibliotherapy: Practice and research.School Psychology International29(2). 161–182) have provided a fertile ground for linking the two disciplines. I propose a cognitive model for the emotionally integrating response to the fairy tale by first introducing Rosenblatt’s (1978.The reader, the text, the poem. Carbondale: Southern Illinois University Press) transactional theory of making meaning from literature, and then in light of this theory, reviewing the psychoanalytic and anthropological evidence for fairy tales’ unique effect on readers. Previous theories explaining the emotional benefits of reading fairy tales have failed to consider characteristics of the genre which are unique to it, and which give fairy tales a hierarchically higher status than other genres due to the distance between the reality they depict, and the current reality of the reader. Transactional theory indicates that this higher status may play a crucial role in the genre’s ability to support healthy emotional development, as readers who establish close personal connections with the hierarchically valorized genre actually become active participants in a reading experience which provides them with comforting, indisputable affirmation of the uprightness of their own moral principles.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".