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Record W3138400093 · doi:10.1093/socpro/spz040

Do College Social Justice Activists Stop When They Graduate? Explaining Volunteer Activist Participation in a Life Course Transition

2020· article· en· W3138400093 on OpenAlexaff
Jonathan Horowitz

Bibliographic record

VenueSocial Problems · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGraduation (instrument)Social justicePublic relationsQualitative propertySociologyQualitative researchLife course approachEconomic JusticeSocial movementPolitical scienceSocial psychologyPsychologyCriminologySocial sciencePoliticsLaw

Abstract

fetched live from OpenAlex

Abstract Why does volunteer participation by college social justice activists decline dramatically following graduation? Using a new, multimethod longitudinal study that spans college and the early post-graduate years, I test existing theory about how changes in time constraints, social support, and organizational opportunities affect volunteer activist participation in social justice movements. Analyzing qualitative data from semi-structured interviews to further understand decreases in activism, I find no evidence showing that changes in time constraints or social support lead to changes in activist volunteering, but there is evidence to support the effects of organizational opportunities. Findings from the qualitative data further emphasize that activist opportunities are easier to access on college campuses, specifically because activism is physically convenient to the potential participant. Future research and practice should explicitly address the spatial proximity of potential activists to social movement opportunities, and should additionally remain sensitive to different needs of activists at different points in the life course.

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)0.006
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.100
GPT teacher head0.356
Teacher spread0.256 · 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 designObservational
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

Citations10
Published2020
Admission routes1
Has abstractyes

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