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Record W4407975476 · doi:10.3390/youth5010021

Research-as-Solidarity, with Youth Leading the Way

2025· article· en· W4407975476 on OpenAlexaff
Jensine Raihan, Niiyokamigaabaw Deondre Smiles, Jaimie G. Vincent, Rwittika Banerjee, Sarah Marquis, Katherine Robitaille, Annie McQuarrie, Melissa Spiridigliozzi, Stephanie Eccles, Jen Gobby

Bibliographic record

VenueYouth · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsConcordia UniversityMcGill UniversityUniversity of VictoriaUniversité LavalDalhousie UniversityUniversity of OttawaCarleton UniversityUniversity of British Columbia
Fundersnot available
KeywordsSolidarityPolitical scienceSociologyLawPolitics

Abstract

fetched live from OpenAlex

Through the lens of youth engaged in the grassroots network Research for the Front Lines (R4FL) in Turtle Island, this essay explores how youth use research as a form of solidarity with marginalized communities in the context of escalating crises, ranging from climate change to systemic inequities. R4FL supports Indigenous, Black, and marginalized groups facing disproportionate impacts from environmental and climate changes. Drawing on the experiences of a diverse group of youth researchers, we examine the barriers posed by traditional academic structures and advocate for a research paradigm that prioritizes community needs, transparency, and accountability. Our discussions highlight how youth leverage our knowledge and research skills in service of social justice and environmental equity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.492
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.710
GPT teacher head0.666
Teacher spread0.044 · 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 teacher head, not a consensus.

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
Published2025
Admission routes1
Has abstractyes

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