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Record W3112601232 · doi:10.5195/jcycw.2020.5

Experiential Teaching and Learning in Child and Youth Care Work:

2020· article· en· W3112601232 on OpenAlexaboutno aff
Varda Mann-Feder

Bibliographic record

VenueJournal of Child and Youth Care Work · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningReflexivityPsychologyExperiential educationPedagogyWork (physics)Medical educationYouth workPositive Youth DevelopmentSociologyMedicinePublic relationsPolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

The Graduate Diploma in Youth Work is in its fifth year at Concordia University in Montreal. In a department committed to experiential teaching and the training of practitioners, a large focus of the program is to immerse students in experiences that prepare them for engaging in reflexive and theoretically informed approaches to practice. The purpose of this article will be to illustrate our program model through four learning activities that are representative of our unique approach to youth worker education. An additional focus will be the ways in which our model and these activities align with the Association for Child and Youth Care Practice competencies.
 
 A model of integrative youth work education was developed in 2015 by Ranahan, Blanchet-Cohen and Mann-Feder to form the basis for an advanced Graduate Diploma in youth work in Montreal, Quebec (Concordia University, n.d.). The purpose of this article is to share four structured experiential learning activities that illustrate this model. Prior to describing the activities, an overview of our approach to integrative youth work will be provided, along with a discussion of how it aligns with the competencies for practice developed by the Association for Child and Youth Care Practice (ACYCP) (Association for Child and Youth Care Practice, 2010).

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.299
Teacher spread0.281 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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