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Record W4392681530 · doi:10.22318/icls2023.360863

“When We’re in Spaces Among People of Colour, Your Ideas Just Flow”: Politicized Trust and Educational Intimacy in Activist Spaces

2023· article· en· W4392681530 on OpenAlexaff
Joe Curnow

Bibliographic record

VenueProceedings. · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Education and Learning Practices
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSociologySolidarityDebriefingGrievancePoliticsCollective identityIdentity (music)Social psychologyMedia studiesEpistemologyAestheticsPsychologyPolitical scienceLaw

Abstract

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This paper examines how relationships of educational intimacy and politicized trust were constructed in an activist community.Bridging theories of politicization and activist becoming with emerging research on the significance of relationships for identity development and solidarity, this paper brings micro-interactional data of how relationships are constructed and why they matter.Tracing a group debrief by members of colour within the Fossil Free UofT activist group allows us to see how humour constructed intimacy alongside intense disclosure and collectivized grievance construction.This contributes to work in the learning sciences to demonstrate the political contexts and consequences of learning and provides tools for activists to organize learning ecologies for educational intimacy and politicized trust. On relationships and learningThe question of how relationships shape learning has animated many of us in the learning sciences over the last decades, and particularly in recent years as attention in our field has shifted to attend more closely to the political and ethical dimensions of learning.Recent research has examined how relations of educational intimacy (Uttamchandani, 2021) and politicized trust (McKinney de Royston & Vakil, 2019) have made specific forms of learning possible, and this work, alongside other interventions toward understanding relationships and politicization is vital in understanding how other possible futures are imagined and enacted through the joint work of community members.This paper theorizes political transformation through a sociocultural lens, arguing that politicization is a learning process, one that unfolds not in the minds of individual participants, but rather as coconstituting processes of development involving the political concepts, practices, epistemologies, and identities of learners as they transform through their engagement in building new possible futures (Curnow, et al., 2020).These theoretical and analytical questions align closely with questions emerging from social movements, where the need to understand how, when, and why some relationships support learning and collective action to change social systems toward more just futures (and why some do not).This question was a persistent undercurrent in the work of FossilFree UofT, a campus-based climate activist group.In their campaign to push the University of Toronto to divest the endowment funds from the 200 fossil fuel companies with the largest reserves, many of the young people engaged in this work dramatically shifted their political orientations.So often, young activists in the campaign wondered why some people "wouldn't learn" and why, by contrast, other spaces felt so nurturing of their political engagement and growth as climate justice activists.In this paper, I take up those questions, asking how relationships of educational intimacy enabled politicization.I use one particularly rich interaction as the basis of analysis, looking at video of an impromptu debrief of people of colour after a tense meeting.In this debrief, we can hear explicit talk about the value of relationships of trust and shared experience, and also see the ongoing unfolding of educational intimacy.Building from Uttamchandani (2021) and Vakil & McKinney de Royston (2019), I argue that the relationships of intimacy and politicized trust enabled participants in the debrief to become politicized, and that the politicization process further entrenched and reinforced their relationships.To make this argument, I begin with a brief overview of recent research on relationships, politics, and learning from sociocultural perspectives.I then provide more detail on the context of Fossil Free UofT before describing the participatory action research project we undertook, the data collection, and the methods of analysis.I then pivot to an analysis of the debrief, highlighting the sensemaking happening in real time, and identifying how the relationships of politicized trust and intimacy make that learning possible. Literature: Relationships, politics, and learning in the learning sciencesResearch that interrogates learning as a sociocultural and interactional accomplishment is foundational to the learning sciences.Within that broad framing, we can look more specifically at work that attends to the affordances for learning that relationships enable.There has been work investigating how friendship shapes learning processes and outcomes among students (Takeuchi, 2016;Jackson, et al.;2020;Vakil & McKinney de Royston, 2019), among social movement organizers (Curnow, et al., 2021;Teeters & Jurow, 2018;Uttamchandani, 2021; Vea, 2018), between teachers and students (Boaler, 2008;McKinney de Royston, et al., 2017), between research collaborators (Vakil, et al., 2016), and among researchers (Jackson, et al., 2020).All of this work recognizes that learning is social, and that who learners are surrounded with matters for what they learn, how they learn, and how

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)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.028
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.071
GPT teacher head0.404
Teacher spread0.333 · 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 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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