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Record W1567246556 · doi:10.5216/vis.v11i2.30685

Entre subjetividades e aparatos pedagógicos: o que nos move a aprender?

2014· article· pt· W1567246556 on OpenAlexaff
Raimundo Martins, Irene Tourinho, Alice Fátima Martins

Bibliographic record

VenueVisualidades · 2014
Typearticle
Languagept
FieldArts and Humanities
TopicArts and Performance Studies
Canadian institutionsFord Motor Company (Canada)
Fundersnot available
KeywordsSubjectivityTrespassSociologySubject (documents)PerceptionSet (abstract data type)Motion (physics)HumanitiesEpistemologyAestheticsArtPhilosophyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

What move us to learn are intangible and invisible subjective processes set in motion in the body and with the body. Social practices and interpersonal forms of relations shape our perceptions of ‘self’ and ‘other’ constructing repertoires that characterize our insertions in the different communities in which we are part. The relations between subjectivity and institutional structures are guided by tensions which extend themselves to the formal conditions of learning. Desires, motivations, affects, doubts and instabilities trespass the timings of learning when being confronted with the resources and pedagogical apparatuses we utilize. Visual culture education proposes to tension and problematize these various trespassing trying to establish transits between subject and collectivity, cultural practices and subjectivities, the thoughtless and the objective, through living experiences which animate us to question and to continue learning as educators and/or students.Keywords: Learning, lived body, subjectivity, visual culture education.

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)0.020
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 score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0110.080
Scholarly communication0.0240.024
Open science0.0020.016
Research integrity0.0030.008
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 head0.038
GPT teacher head0.290
Teacher spread0.252 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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