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Record W4401970360 · doi:10.1163/23644583-bja10056

Pushing the Limits of Participatory Video: Exploring Transgressive Voices through Researcher-Participant Minor Video-Making as a Non-Representational Practice

2024· article· en· W4401970360 on OpenAlexaff
Masayuki Iwase

Bibliographic record

VenueVideo Journal of Education and Pedagogy · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsElectronic Arts (Canada)
Fundersnot available
KeywordsParticipant observationTransgressiveMinor (academic)Citizen journalismPsychologySociologyCommunicationComputer scienceArtSocial scienceWorld Wide WebHumanities

Abstract

fetched live from OpenAlex

Abstract This article inquires into participatory researchers’ ‘representational’ practices in relation to sharing power with minority participants through collaborative video-making processes. The author argues that there is a limit to attaining such a political and ethical end because a ‘representational’ logic seemingly operates as the theoretical and methodological underpinning for participatory video, which undercuts its ability to represent the voices of collaborators. This article takes into account Shannon Walsh’s (2014) emphasis that “if participatory video is to be a significant method within a project for social change, we must push its limits, and its politics” (p. 140). This article does so by drawing on a ‘non-representational’ approach (Vannini, 2015) and the concept of ‘transgressive voices’ (Jackson & Mazzei, 2009). The author discusses his experience with Deleuze-inspired ‘minor video-making’ as a relational and affective practice in which participants, tangible and intangible research objects and environments, and the researcher himself became relationally entangled to falsify any predetermined essentialized identity and to compose a powerful new body. In the unfolding of transgressive voices in the specific liminal space or moments during the minor video-making, the author ‘intensively and immanently reads’ (Masny & Cole, 2012) the entanglements during storyboarding, rehearsing, shooting, and editing.

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)0.011
metaresearch head score (Gemma)0.019
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 score0.435
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
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 head0.803
GPT teacher head0.702
Teacher spread0.101 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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