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Record W2737661171 · doi:10.1177/1609406917717345

In This Together

2017· article· en· W2737661171 on OpenAlexafffundabout
Jenny Reich, Linda Liebenberg, Mallery Denny, Hannah Battiste, Angelo Bernard, Kevin M. Christmas, Ronald Dennis, Dionne Denny, Ivan Knockwood, Raylene Nicholas, Hugh Paul

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsNova Scotia HospitalDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsParticipatory action researchCitizen journalismPublic relationsAccountabilityFlexibility (engineering)Process (computing)SociologyWork (physics)Action researchAction (physics)Political sciencePedagogyEngineeringComputer scienceManagement

Abstract

fetched live from OpenAlex

This article explores what it means to engage youth in meaningful dissemination of research findings. To do so, the authors (a group of academic researchers and youth collaborators, aged 14–18) consider their experience working together on the Spaces & Places research project, a participatory visual methods research program that took place in Eskasoni, a Mi’kmaq community in rural Nova Scotia, Canada. Over the course of the project, we developed a strong sense of relational accountability. Reflecting on our experiences, we believe that this is central to the development of a dissemination process that is meaningful and engaging. To reflect on youth perspectives and experiences of the project’s dissemination process, we use a participatory action research technique—the Socratic Wheel—to explore six factors that contribute to a meaningful dissemination process: The degree to which the project is relationship building, strengthening, rewarding, able to reach our intended audience, provides opportunities moving forward, balances structure with flexibility, and allows youth to have a sense of ownership over their work.

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)0.011
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.630
GPT teacher head0.690
Teacher spread0.061 · 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 designTheoretical or conceptual
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

Citations38
Published2017
Admission routes3
Has abstractyes

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