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Record W4405025616 · doi:10.1080/2159676x.2024.2437407

Infusing ‘post’ thinking in qualitative training using material-performative pedagogies

2024· article· en· W4405025616 on OpenAlexaff
Martin Camiré

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPerformative utteranceQualitative researchTraining (meteorology)PsychologyPedagogyMathematics educationSociologyAestheticsArtSocial scienceGeography

Abstract

fetched live from OpenAlex

The paper infuses post thinking in qualitative training by providing a concrete example of how students can learn to inquire through the posts in a graduate-level methods course. The paper begins by storying ‘the qualitative course’, outlining its structure and rationale. The qualitative course’s main post assignment (i.e. psychogeographic walk and performance) is then detailed and positioned as a material-performative pedagogy offering encounters that can transform students if they open themselves to the vulnerabilities and opportunities of inquiring on-the-move. Key next steps for the qualitative course are presented in terms of changing the name of the assignment, adding an object assignment, exposing students to the virtues of errant reading, inquiring as an act of creation and inquiring as epistemic witnessing. Implications for qualitative inquiry in sport, exercise, and health are offered that include attending to the relational ongoingness of existence, creating alternate understandings of understanding, and attuning to presences/absences. The paper concludes with thoughts on our collective response-ability to promote ontological pluralism. How we train the next generation of qualitative inquirers to play the ontological politics game has immense implications for presencing the manifold thoughts, concepts, and worlds that remain endarkened.

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)0.191
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1910.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0000.002
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 head0.923
GPT teacher head0.797
Teacher spread0.126 · 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 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
Published2024
Admission routes1
Has abstractyes

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