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Record W2067803312 · doi:10.1177/0895904814550078

Video Making, Production Pedagogies, and Educational Policy

2014· article· en· W2067803312 on OpenAlexaff
Suzanne Smythe, Kelleen Toohey, Diane Dagenais

Bibliographic record

VenueEducational Policy · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsWorkaroundLiteracyCritical literacyActor–network theorySociologyCritical theoryAffordancePedagogyProduction (economics)Computer scienceMathematics educationPolitical scienceSocial sciencePsychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

The promise of “21st century learning” is that digital technologies will transform traditional learning and mobilize skills deemed necessary in an emerging digital culture. In two case studies of video making, one in a Grade 4 classroom, and one in an adult literacy setting, the authors develop the concept of “production pedagogies” as complex multiliteracies embedded in video production oriented to meaningful social ends. Drawing upon concepts of translation in Actor Network Theory (ANT) and the “workaround,” the authors trace how in spite of the imaginary of “21st Century Literacy,” policy regimes privileged networks oriented to “minimal proficiency” print literacy. They theorize that the workarounds in which practitioners engaged illuminate three nodes or sites of action to strengthen production pedagogy networks: how learners are defined or problematized in literacy projects, how people get access to powerful digital literacy tools for learning, and how time-space regimes of traditional schooling are reconfigured.

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)0.009
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0090.031
Scholarly communication0.0110.015
Open science0.0020.007
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.033
GPT teacher head0.329
Teacher spread0.295 · 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

Citations24
Published2014
Admission routes1
Has abstractyes

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