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Record W4206795927 · doi:10.3389/fcomm.2021.786465

Developing a Choice-Based Digital Fiction for Body Image Bibliotherapy

2022· article· en· W4206795927 on OpenAlexafffund
Christine Wilks, Astrid Ensslin, Carla Rice, Sarah Riley, Megan Perram, K. Alysse Bailey, Lauren Munro, Hannah Fowlie

Bibliographic record

VenueFrontiers in Communication · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of AlbertaUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNarrativeAffordanceBinary oppositionTransformative learningBibliotherapyAestheticsAgency (philosophy)PsychologyVisual artsArtLiteratureEpistemologyCognitive psychologyPsychotherapistDevelopmental psychologyPhilosophy

Abstract

fetched live from OpenAlex

Body dissatisfaction is so common in the western world that it has become the norm, especially among women and girls. Writing New Body Worlds is a transdisciplinary research-creation project that aims to address these issues by developing an interactive digital fiction for body image bibliotherapy. It is created with the critical co-design participation of a group of young women and non-binary individuals (aged 18–25) from diverse backgrounds, who are representative of its intended audience. This article discusses how our participant research influenced the creative development of the digital fiction, its characters and its novel ludonarrative or story-game design. It theorizes how the specific affordances of a choice-based interactive narrative, that situates the reader-player in the mind of the fictional protagonist, may lead to enhanced empathic identification and agency and, therefore, a more profoundly immersive and potentially transformative experience. This process of “diegetic enactment” is where we postulate the therapeutic value lies: an ontological oscillation between the reader-player’s mind and the fictional mind, which may induce the reader-player to reflect upon, and perhaps subtly alter, their own body image.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.338
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes2
Has abstractyes

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