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Record W7135160755

In Defense Of Friction: Designing for agency through constructivist entanglements

2023· article· en· W7135160755 on OpenAlexaff
Jennifer Briselli

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2023
Typearticle
Languageen
FieldNeuroscience
TopicEmbodied and Extended Cognition
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsCognitive reframingAgency (philosophy)Transformative learningConstructivist teaching methodsPresentation (obstetrics)Experiential learningWork (physics)Phenomenon
DOInot available

Abstract

fetched live from OpenAlex

This article seeks to interrogate and reframe the role of friction in design from a separate and isolated phenomenon that hampers a system to one that emerges from the entangled interactions between objects and their environments and serves as a valuable feedback cycle within the system. This feedback can teach us how to facilitate more equitable experience ecosystems where agency is organically empowered to emerge instead of granted or allowed. To this end, design is overdue for a constructivist turn as a counterbalance to cognitivist and behaviourist modes of thinking. Constructivist approaches necessarily rejigger the relationships between designer and designed and reframe their interactions as emergent entanglements rather than linear dynamics. Drawing on the work of Dewey, Friere, Mezirow, Barad, and others, this presentation explores and synthesises key ideas from agential realism, transformative learning theory, and liberation pedagogy through a constructivist, systems-oriented lens to identify themes with great potential for the work of systemic designers.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.905

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.181
GPT teacher head0.358
Teacher spread0.177 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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