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Envisioning healthy futures: Youth perceptions of justice-oriented environments and communities in Northern British Columbia Canada

2022· article· en· W4281722979 on OpenAlexaffabout
Onyx Vanessa Sloan Morgan, Kimberley Thomas, Laura McNab‐Coombs

Bibliographic record

VenueHealth & Place · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Northern British ColumbiaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsPhotovoiceEnvironmental justiceIndigenousEconomic JusticeSociologyPerceptionFutures contractPerspective (graphical)Youth studiesYouth engagementCriminologyPublic relationsGender studiesPolitical sciencePsychologyEcologyEconomic growth

Abstract

fetched live from OpenAlex

Through an anti-colonial and critical race theoretical framework as well as arts-based methods (photovoice) that engage Indigenous and non-Indigenous youth, we explore the question: what do youth perceive as healthy and just environments and communities? Youth identified two overarching, strength-based messages: Firstly, youth demonstrate the need for a structural-level analysis of the conditions that influence individual-level outcomes of environmental health. Secondly, youth perspectives on healthy and justice-oriented environments and communities challenge environmental health scholars to consider youth as powerful actors. Youth perspectives of healthy and justice-oriented communities present a necessarily structural perspective to consider not only the impacts of environmental decision-making on health, but the conditions that have allowed for harmful impacts. In doing so, youth demonstrate the need for intersectional and complex understandings of health and wellbeing when discussing the environment. And, as we argue here, challenge us as scholars of environmental health to do the same.

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)0.002
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 score0.042
Threshold uncertainty score0.307

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.005
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.195
GPT teacher head0.492
Teacher spread0.297 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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