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Record W4283804239 · doi:10.1177/14614448221105847

Hybrid activism under the radar: Surveillance and resistance among marginalized youth activists in the United States and Canada

2022· article· en· W4283804239 on OpenAlexfundaboutno aff
Ashley Lee

Bibliographic record

VenueNew Media & Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaStanford Center on Philanthropy and Civil SocietyWeatherhead Center for International Affairs, Harvard UniversitySpencer Foundation
KeywordsPoliticsSocial mediaPolitical activismSociologyResistance (ecology)Political scienceSocial controlRace (biology)Gender studiesPublic relationsSocial scienceLaw

Abstract

fetched live from OpenAlex

Social media and digital platforms have become essential tools for the new generation of youth activists. However, these tools subject youth to both new (and old) forms of surveillance and control. Drawing on in-depth interviews and social media walkthroughs with 61 youth activists, I examine hybrid tactics that these youth employ to resist surveillance and other forms of digitally mediated control as they participate in politics and social movements. I show that even in democracies like the United States and Canada, for individuals along intersecting axes of marginalization (e.g. race, gender), public political acts do not capture the full range of young people’s political repertoires. Young people, especially those from marginalized groups, adopt hidden, under-the-radar tactics in response to pressures of social, state, and corporate surveillance. I develop the concept of “digital infrapolitics” referring to the ways in which digital politics and activism go below the radar under surveillance and control.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.045
Threshold uncertainty score0.324

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0220.009
Scholarly communication0.0080.002
Open science0.0020.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.260
Teacher spread0.237 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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