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Record W4283464091 · doi:10.36510/learnland.v15i1.1069

Evoking Losing and Finding Community in Drama: A Methodology-in-Motion for Pandemic Times

2022· article· en· W4283464091 on OpenAlexaffvenue
Kathleen Gallagher, Nancy Cardwell, Munia Debleena Tripathi

Bibliographic record

VenueLEARNing Landscapes · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEthnographyActive listeningDramaReciprocity (cultural anthropology)SociologyThe artsMotion (physics)Dynamics (music)PandemicEconomic JusticeMedia studiesPublic relationsVisual artsPedagogyCoronavirus disease 2019 (COVID-19)Political scienceSocial scienceArtAnthropologyComputer scienceCommunication

Abstract

fetched live from OpenAlex

Our article explores the impact of the global health pandemic on our five-year, multi-sited, collaborative ethnographic study titled Global Youth (Digital) Citizen-Artists and their Publics: Performing for Socio-Ecological Justice (2019-2024). We illustrate how our arts-led, youth-driven ethnographic ”methodology-in-motion” responded to a destabilized world by planning, listening, and seeing differently across local and global research contexts through virtual fieldwork. By focusing on reciprocity and the relational, we examine how researchers, youth participants, and global collaborators, managed to ”lose” and ”find” each other through creative, artistic encounters.

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)0.024
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.597
GPT teacher head0.596
Teacher spread0.001 · 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.

Study designObservational
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

Citations0
Published2022
Admission routes2
Has abstractyes

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