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Record W2464022153

Learning Activism: The Intellectual Life of Contemporary Social Movements

2016· article· en· W2464022153 on OpenAlexvenueaboutno aff
Matthew Waugh, Angelina S. Lee

Bibliographic record

VenueCanadian Journal of Education / Revue canadienne de l éducation · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsAppropriationSocial movementSociologySocial movement theoryPower (physics)Social learningMedia studiesPoliticsSocial scienceEpistemologyPolitical sciencePedagogyLaw
DOInot available

Abstract

fetched live from OpenAlex

Learning Activism: The Intellectual Life of Contemporary Social Movements by Aziz Choudry North York, Ontario: University of Toronto Press, 2015, 199 pages ISBN: 978-1-4426-0790-3 (paperback) For Aziz Choudry (2015), author of Learning Activism: The Intellectual Life of Contemporary Social Movements, learning and knowledge production, as well as theory and strategy development among social are best understood through engagement and dialogue that occur within the movements themselves. As a means to produce knowledge that is relevant to the social movement, learning comes through action and experience, the behind-the-scenes work where activists organize, negotiate and deliberate, and educate and learn from movement successes and pitfalls within formal and informal spaces. Learning in many ways, attempts to bridge the divide between social movement activists and out-of-the-box scholars who study or view such movements as sterile environments containing who may be thought of incapable of theorizing or producing knowledge. The first chapter, Knowledge Production, Learning, and Education in Social Movement Activism, begins by directly stating that the future is influenced through present and past struggles, by the choices made and theories that are developed by individuals or collectives, by ordinary people and so-called experts. The author makes clear that social movements can fail in the absence of theory that explains economic conditions and power struggles. Yet, important point often overlooked by activist scholars specifically, as well as educational researchers more broadly, is one of Choudry's (2015) finest critiques, who does the theorizing? While arguing against intellectual appropriation of struggles in social the author challenges academics and activists to think about and legitimate the work done in day-to-day social movement practices. Without recognition and validation of knowledge production and learning that come about through social movement practices and struggles in social activism, theory development and knowledge production become monopolised by institutional experts. The second chapter, Critiquing the Study of Social Movements: Theories, Knowledge, History, and Action, examines the intersection between theory development, understanding social movement, and knowledge production. This chapter brings a sharp critique of not only the historical social movement scholarship that perceived activism as merely a social problem, and deviant acts imposed upon a stable society, but the perception of activists displaying irrational behaviours and the movement itself being abnormal has endured to this day. From Occupy Wall Street protests in Zuccotti Park to the 2012 Quebec student strike, concerns brought forward by activists have often been dismissed in favour of maintaining current power structures while negatively characterising protestors through media outlets, elected officials, and certain segments of the public. Aziz Choudry argues against the reflexive application of theories that have been fashioned in the First World/North towards colonized contexts without reflecting on the unique lived experiences, struggles, and social relations of people in the Third World/ South. This obligates not only social activist researchers to reject an overattachment to paradigms, typologies, and criteria for describing movements, but for social science scholars as well who may continue to codify and rely on specialized language and knowledge that only acts as a barrier to meaningful discussions between scholars and research participants (Choudry, 2015, p. …

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)0.003
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.961
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0090.030
Scholarly communication0.0150.009
Open science0.0010.007
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.054
GPT teacher head0.274
Teacher spread0.220 · 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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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