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Record W2538941183 · doi:10.1177/0034523716664603

Towards a rhythmic account of working together and taking part

2016· article· en· W2538941183 on OpenAlexaff
Stephanie Springgay

Bibliographic record

VenueResearch in Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMateriality (auditing)MaterialismSociologyDilemmaEpistemologyPoliticsAestheticsSocialityDemocracyPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

The idea that the world is composed of moving and constantly transforming materialities that are vibrant, quivering, and indeterminate has shifted how we think about human and non-human relations. Matter is not a stable entity, but one that is continuously vibrating and differentiating. This materialism is crucial for thinking about possible futures of educational research. In this paper, I turn to the materiality of rhythm, movement, and affect to suggest a more vital understanding of participation and thus politics. The paper takes as its starting point contemporary art that is often framed as collaborative, relational, or participatory. However, the dilemma with such art is that under the guise of collaboration, sociality, and inclusion participation becomes a method of appeasement as opposed to any real process of transformation. Participation is often instrumentalized in order to minimize conflict and friction, and reifies utopic notions of emancipation, voice, and agency. In this short essay, I want to consider participation's materiality. Rather than thinking of participation as democratic decision-making, can we imagine participation as an intrinsic mode of being where taking part is not something to opt into or out of, but a vital lived relation.

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0090.043
Scholarly communication0.0130.016
Open science0.0030.008
Research integrity0.0040.005
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 head0.248
GPT teacher head0.482
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations9
Published2016
Admission routes1
Has abstractyes

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