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Record W601262139 · doi:10.7202/1044589ar

Growing Up with Expectations. Better Understanding the Expectations of Community Partners in Participatory Action Research Projects

2018· article· en· W601262139 on OpenAlexaffvenueabout
Doug Ragan, Clarissa Wilkinson

Bibliographic record

VenueLes ateliers de l éthique · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsParticipatory action researchEquity (law)Action researchCitizen journalismAction (physics)Public relationsProcess (computing)SociologyState (computer science)Political sciencePedagogyComputer science

Abstract

fetched live from OpenAlex

This paper challenges the assumption that youth and youth agencies are in a condition of equality when entering a participatory action research (PAR). By asserting that it is not a state of equality that practitioners nor youth should assume nor be immediately striving for, but a consistently equitable process, this article draws from and reflects on the relationship between young people and researchers who have used a PAR methodology in action oriented projects. Using the UNESCO Growing up in Cities Canada project as a case example, this review extrapolates from and reflects on challenges faced by the project as a whole. Using semi-structured interviews to explore the roles of adults and youth, a number of strategies are highlighted as the techniques used to overcome these challenges. The discussion concludes with further reflection on the complexities of equality and equity, recommending a number of actions that have the potential to create an equitable environment in PAR projects similar to the one examined.

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)0.139
metaresearch head score (Gemma)0.159
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.861
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1390.159
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0090.017
Scholarly communication0.0180.020
Open science0.0030.016
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0030.001

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 head0.849
GPT teacher head0.658
Teacher spread0.191 · 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.

Study designQualitative
DomainMethods
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
Published2018
Admission routes3
Has abstractyes

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