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Record W1915259724 · doi:10.18357/ijcyfs32-3201210868

PEDAGOGICAL ENCOUNTERS OF THE CASE-BASED KIND

2012· article· en· W1915259724 on OpenAlexaffvenue
Heather Sanrud, Patti Ranahan

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsMirroringContext (archaeology)PedagogyPsychologyProcess (computing)SociologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

Child and youth care (CYC) practice is diverse, complex, and contextualized. Pedagogical approaches to preparing CYC professionals in pre-service education programs require learning activities that recognize the “inter-subjective, contingent, and context dependent character of everyday CYC work” (White, 2007, p. 241). Case-based learning activities are advantageous in preparing future professionals for the complexities of everyday CYC work. These activities provide students with an opportunity to explore CYC practice in an authentic way while being supported by instructors who model, coach, and engage students throughout the process. This paper describes the organic evolution of the case of “Allan” at the beginning of a year-long CYC course facilitated by a team of instructors. The case of Allan continued to develop throughout the course in an authentic fashion mirroring the realities of CYC work with individuals and families over time. Allan provided a way of situating course content as well as supporting students’ application of new knowledge and skills to practice situations.

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)0.007
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.014
Scholarly communication0.0060.006
Open science0.0020.012
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.134
GPT teacher head0.326
Teacher spread0.192 · 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.

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

Citations13
Published2012
Admission routes2
Has abstractyes

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