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Record W2412072507 · doi:10.1111/josi.12168

Acting in Solidarity: Cross‐Group Contact between Disadvantaged Group Members and Advantaged Group Allies

2016· article· en· W2412072507 on OpenAlexaff
Lisa Droogendyk, Stephen C. Wright, Micah E. Lubensky, Winnifred R. Louis

Bibliographic record

VenueJournal of Social Issues · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsDisadvantagedSocial psychologyPrivilege (computing)SolidarityAutonomyFeelingPsychologySociologyGender studiesPolitical sciencePoliticsLaw

Abstract

fetched live from OpenAlex

The actions of advantaged group activists (sometimes called “allies”) are admirable, and they likely make meaningful contributions to the movements they support. However, a nuanced understanding of the role of advantaged group allies must also consider the potential challenges of their participation. Both in their everyday lives and during their activist work, advantaged group allies are especially likely to have direct contact with disadvantaged group members. This article considers when such contact may harm rather than help resistance movements by disadvantaged groups. We also suggest that to avoid these undermining effects, advantaged group allies must effectively communicate support for social change, understand the implications of their own privilege, offer autonomy‐oriented support, and resist the urge to increase their own feelings of inclusion by co‐opting relevant marginalized social identities.

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)0.007
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 score0.016
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.007
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0020.003
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 head0.030
GPT teacher head0.395
Teacher spread0.365 · 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

Citations214
Published2016
Admission routes1
Has abstractyes

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