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Record W4398253863 · doi:10.1177/10778004241253263

Keeping the Conversation Going: Rendering Each Other Capable While Creating Zines

2024· article· en· W4398253863 on OpenAlexaff
Lieve Carette, Alise de Bie, Kate Brown, Elisabeth De Schauwer

Bibliographic record

VenueQualitative Inquiry · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Philosophies and Pedagogies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConversationRendering (computer graphics)SociologyAutoethnographyAestheticsComputer scienceEpistemologyArtVisual artsMedia studiesComputer graphics (images)CommunicationPhilosophyAnthropology

Abstract

fetched live from OpenAlex

In response to Lee de Bie and Kate Brown’s webinar on neuroatypicality in academia, Lieve Carette and Lee de Bie delve into the concept of “relational access” and its transformative influence on neurodivergent relationships, overcoming obstacles and expanding possibilities of support. Drawing inspiration from the creative initiatives of Mad and neurodivergent students and staff reshaping the academy, the authors share insights from their 6-year friendship, exploring the challenges of navigating university through neuroatypicality. Their interconnected reflections underscore the importance of facilitating the creation of the zine “Outliers” in shaping their dialogues. Within the context of Qualitative Inquiry, this article indirectly explores zines as an academic methodology, emphasizing the integral role of the intimate relationship in zine project development and personal and professional growth. The paper concentrates on the zine’s impact within their relationship, accentuating its modest contribution to the project’s inception compared with its substantial significance in their lives and personal growth.

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)0.015
metaresearch head score (Gemma)0.023
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0150.025
Scholarly communication0.0120.019
Open science0.0020.027
Research integrity0.0020.007
Insufficient payload (model declined to judge)0.0080.002

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 head0.359
GPT teacher head0.509
Teacher spread0.151 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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