MétaCan
Menu
Back to cohort
Record W3088188489 · doi:10.1177/1609406920958974

Creating Space for Youth Voice: Implications of Youth Disclosure Experiences for Youth-Centered Research

2020· article· en· W3088188489 on OpenAlexafffund
Roberta L. Woodgate, Pauline Tennent, Sarah Barriage

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Manitoba
FundersManitoba Health Research Council
KeywordsTerminologyAgency (philosophy)Context (archaeology)Youth studiesAutonomyPublic relationsPhraseSpace (punctuation)Positive Youth DevelopmentSociologyPsychologyPolitical scienceGender studiesDevelopmental psychologyLinguistics

Abstract

fetched live from OpenAlex

This paper examines youth’s disclosure experiences within the context of chronic illness, drawing on examples from IN•GAUGE, an on-going research program led by Dr. Roberta L. Woodgate. Youth’s descriptions of their disclosure experiences provide valuable insights into the ways in which they use their voice in everyday life. This examination of the disclosure experiences of youth offers a lens through which the concept of youth voice in the research process can be understood and youth’s agency foregrounded. We present implications for researchers, ethics boards, funding agencies, and others who engage in youth-centered research, and offer alternative terminology to use in characterizing the elicitation and dissemination of youth voice in the research process. We contend that conceptualizing such efforts as giving youth voice has the potential to discredit the significant agency and autonomy that youth demonstrate in sharing their stories, perspectives, and opinions within the research context. We advocate for the adoption of the phrase of providing or creating space for youth voice, as one alternative to the phrase giving youth voice

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)0.048
metaresearch head score (Gemma)0.081
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.432
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0480.081
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.971
GPT teacher head0.813
Teacher spread0.157 · 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; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreMethods

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

Citations25
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Qualitative MethodsSame topicParticipatory Visual Research MethodsFrench-language works237,207