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Record W4390231785 · doi:10.1177/02724316231223527

Post-Pandemic Engagement of Youth in Virtual Environments: Reflections and Lessons Learned From the Development of a Youth Education Program

2023· article· en· W4390231785 on OpenAlexafffund
Jennifer Donnan, Rachel Howells, Dalainey H. Drakes, Lisa Bishop

Bibliographic record

VenueThe Journal of Early Adolescence · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of OttawaMemorial University of Newfoundland
FundersCanadian Institutes of Health Research
KeywordsAutonomyPositive Youth DevelopmentPublic engagementVariety (cybernetics)Youth studiesPandemicDiversity (politics)Public relationsModalitiesYouth engagementPsychologySociologyCoronavirus disease 2019 (COVID-19)Political scienceSocial scienceComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic introduced new landscapes for research and public engagement participation. This shift was accompanied by significant challenges and unique opportunities for engaging youth as active participants and collaborators. This commentary will reflect on insights gained from conducting a variety of virtual youth engagement activities during the pandemic, within a rights-based and empirical approach. The team reflected on challenges, opportunities, and suggestions for engaging youth as participants and collaborators in research using virtual platforms. This commentary outlines opportunities for growth and challenges worthy of consideration for future virtual youth engagement activities. These considerations are put forth with the goal of upholding autonomy, diversity, and amplifying the voices of youth in research through virtual environments. Considering our insights on engaging youth, we hope to contribute to the expanding field of youth engagement, and advance future research that utilizes virtual modalities.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.385
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.127
GPT teacher head0.372
Teacher spread0.245 · 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.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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