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Record W4240354322 · doi:10.32920/14636535

Poised to advocate: the pedagogy of the lightning talk in child and youth care education

2021· preprint· en· W4240354322 on OpenAlexaffabout
Johanne Jean‐Pierre, Sabrin Hassan, Asha Sturge, Kiaras Gharabaghi, Megan A. Lewis, Jonathan Bailey, Melanie Panitch

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsToronto Metropolitan UniversityYorkville UniversityUniversity of Toronto
Fundersnot available
KeywordsPraxisPedagogyMetropolitan areaPsychologyChild careMedical educationPolitical scienceNursingMedicine

Abstract

fetched live from OpenAlex

<p>Advocacy is an integral part of child and youth care workers’ roles and a significant component of child and youth care politicized praxis and radical youth work. Drawing from the qualitative data of a mixed-methods study conducted in 2019 at a Canadian metropolitan university, this study seeks to unpack how the pedagogy of the lightning talk can foster advocacy skills to effectively and spontaneously speak out with and on behalf of children, youth, and families in everyday practice when an unforeseen systemic challenge or barrier arises. A purposive sample of 70 undergraduate students was recruited in two child and youth care courses, both of which required students to present a lightning talk. Participants completed an online questionnaire with closed-ended and open-ended questions in order to share their perspectives of the pedagogy of the lightning talk. The findings show that the lightning talk fosters twenty-first century and metacognitive skills and, most importantly, advocacy skills.</p> <p>Keywords: pedagogy, lightning talk, oral presentations, advocacy, child and youth care, youth work</p>

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.324
Teacher spread0.306 · 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 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

Citations4
Published2021
Admission routes2
Has abstractyes

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