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Record W3048427462 · doi:10.1177/1077800420948105

Considering Response Communities: Spaces of Appearance in Narrative Inquiry

2020· article· en· W3048427462 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueQualitative Inquiry · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicNarrative Theory and Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNarrativeCourageSociologyDisaster responseField (mathematics)Narrative inquiryVulnerability (computing)InterimEpistemologyAestheticsHistoryPolitical scienceLinguisticsArtComputer scienceEmergency management

Abstract

fetched live from OpenAlex

We focus on the place of response communities in narrative inquiry. While we have always engaged in response communities, the theoretical basis for their importance is not well developed. Experiences in response communities allow us to be, and live, in the world in ways marked by courage and vulnerability. Response communities are created to help us further our field texts and interim and final research texts and to provide insight into our present and future stories. Response communities help us understand ourselves, within what Arendt calls public spaces. Arendt helps us conceptualize response communities as spaces of appearance.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.250
GPT teacher head0.390
Teacher spread0.140 · 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