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Record W2934378034 · doi:10.1111/bjet.12773

The craftivists: Pushing for affective, materially informed pedagogy

2019· article· en· W2934378034 on OpenAlexfundno aff
Jennifer Rowsell, Mark Shillitoe

Bibliographic record

VenueBritish Journal of Educational Technology · 2019
Typearticle
Languageen
FieldComputer Science
TopicDigital Education and Society
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaEuropean CommissionUniversity of California, Santa Cruz
KeywordsEmbodied cognitionFeelingMaterialismAgency (philosophy)Space (punctuation)Focus (optics)Materiality (auditing)SociologyAestheticsPedagogyPsychologyEpistemologySocial psychologyComputer scienceSocial scienceArt

Abstract

fetched live from OpenAlex

Abstract The idea of making as a form of activism or, as we refer to it in this paper, craftivism , underpins our ambition to transform pedagogical environments into spaces of possibility through sensory and affective making practices. A craftivist agenda pushes for open teaching and learning with materials so that students can inhabit a what if space which ruptures institutional time and space. Beginning with a theorization of disruptive making and dissensus and then moving on to foreground a materialist stance on making, the paper illustrates how students experience materialities across two very different classroom environments and how these experiences led to greater presence, voice and agency for students. There is an underlying focus in the paper on embodied responses and affective flows. After theorizing craftivism, there are three sections that explore making, teaching and feeling through craftivist, postdigital methods.

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)0.013
metaresearch head score (Gemma)0.012
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 score0.017
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0040.063
Scholarly communication0.0170.013
Open science0.0020.012
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.296
Teacher spread0.289 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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