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To Walk the Same Road: Convivial Possibilities and Ethical Affordances in Borderlands Schooling

2022· article· en· W4313436177 on OpenAlexvenueno aff
Brendan H. O’Connor

Bibliographic record

VenueAnthropologica · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsAffordanceSalience (neuroscience)SociologyMoralityRelation (database)ContradictionEthnomethodologySocial psychologyEpistemologyPsychologySocial scienceCognitive psychology

Abstract

fetched live from OpenAlex

In this article, I explore how teachers and students in two distinct regions of the US-Mexico borderlands, southern Arizona and south Texas, treated social difference as an ethical affordance (Keane 2014) or a resource for moral stancetaking. Inspired by work in the anthropology of morality and ethnomethodological analyses of “accountable moral choice” (Heritage 1984, 76) in interaction, I examine how the salience of social difference can become an imaginative affordance for probing experiences of and possibilities for living with difference. When axes of social differentiation became relevant to ongoing interaction, participants used them to frame their own actions or others’ actions as morally admirable, justifiable, or questionable. At times, they did so in ways that foreclosed possibilities for conviviality; at other times, their “ordinary” ethical activity (Das 2012) suggested new possibilities for dealing with social difference in diverse contexts. The analysis testifies to the “internally riven” nature of the moral universe (Keane 2011, 173)—the different stances available to be taken up, even in relation to the same people and the same objects of evaluation—and underscores that conviviality is better viewed not as a lasting state of affairs, but as a provisional interactional achievement and a site of struggle and contradiction.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.084
GPT teacher head0.414
Teacher spread0.329 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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