Fantasy, Fiction, and the Foundations of “Relational Authenticity”: J. M. Barrie's<i>Peter and Wendy</i>and Søren Kierkegaard's<i>Fear and Trembling</i>Revisited
Bibliographic record
Abstract
Abstract This article examines notions of authenticity, which have traditionally centered on the individual, and explores instead a view of authenticity that is “reciprocal” (Zahavi 2001; Rokotnitz 2014) and “relational” (Gallagher, Morgan, Rokotnitz 2018). Investigating how children may cultivate a sense of authenticity by engaging in two forms of imaginative projection—make-believe play and fictional stories—I explore how the co-constitution of self and other(s) advances the Existentialist project. Anchoring developmental data, philosophical argumentation, and critical analysis in a literary text written for and about children—J. M Barrie's Peter Pan—I unpack how play and fiction may contribute to authentic self-becoming by fostering social interchange and how this dynamic is made available for interrogation in Barrie's novel. The analysis presented here reveals Wendy to be the heroine of Barrie's novel, reconfiguring its implications for literary scholarship, and also explicates why categories first articulated by Søren Kierkegaard, such as “the single individual” (Fear 67), one's “absolute relation to the absolute” (78), and “witnessing” (104), are still worth pursuing in a post-postmodern twenty-first-century context. Above all, I re-define authentic self-becoming as fundamentally and necessarily relational.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".