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Record W2567477166 · doi:10.7202/1070554ar

Pedagogy of Attention: Subverting the Strong Language of Intention in Social Justice Education

2020· article· en· W2567477166 on OpenAlexaffvenue
Sarah J. DesRoches

Bibliographic record

VenuePhilosophical Inquiry in Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCritical and Liberation Pedagogy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEconomic JusticeSocial justicePedagogyAction (physics)NothingCritical pedagogySociologySocial pedagogySpace (punctuation)EpistemologySocial changeLawSocial sciencePolitical scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

In this paper, I explore the possibility of social justice education as pedagogy of attention rather than simply pedagogy of intention. Drawing on Gert Biesta’s (2010) concept of “strong” education, I begin by explaining how the language of intention in social justice education relies on a discourse in which “in-puts” will result in specific and immediate “out-puts.” In this sense, social justice education can proceed too quickly to action-oriented imperatives. Following this, I take up Jan Masschelein’s (2010) notion of poor pedagogy: pedagogy that requires nothing more than paying attention to argue that creating a space in which the only goal is to pay attention offers the potential of producing a shift in how social justice education proceeds. Pedagogy of attention subverts the primacy of pedagogy of intention by making an important contribution to social justice education, presenting the world in a way that is not contained by the frames (limitations) of what students are told exists, allowing for the possibility of transformation.

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)0.001
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: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.270

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.118
GPT teacher head0.455
Teacher spread0.337 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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