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Record W4220732262 · doi:10.18432/ari29626

Puppets Know Best

2022· article· en· W4220732262 on OpenAlexaffvenue
Lauren Michelle Levesque, Cécile Rozuel

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsLiminalityAestheticsTransformative learningStorytellingIdentity (music)Space (punctuation)SociologyCarvingThe artsSymbol (formal)PsychologyVisual artsNarrativePedagogyArtLiteratureComputer science

Abstract

fetched live from OpenAlex

This article addresses the struggle of crafting a recognized professional scholarly identity, and reflects on the significance of puppets to interrupt this struggle, assert one’s voice, and creatively occupy one’s space. Our interdisciplinary contribution aims to extend conversations on the realities of academic life that are often muted or diluted such as anxiety, self-doubt, weariness and failure, with implications for creative research practices. We engage the aforementioned realities through a mix of creative and whimsical writing styles (e.g., human-puppet dialogues; poetry; reflection), leveraging insights from the Jungian psychological approach to archetypal symbol and the imagination as well as transformative arts-based approaches involving storytelling, voice, and liminal space. After exploring our own experiences carving out space as creative and reflective scholar-practitioners, we discuss two examples where puppets disrupted the status quo of particular academic settings and provided opportunities for different, more spontaneous forms of engagement with the self and with others.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.165
Threshold uncertainty score0.551

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0070.011
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1650.052

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 head0.090
GPT teacher head0.446
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2022
Admission routes2
Has abstractyes

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