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Record W4244030499 · doi:10.53935/2641-533x.v2i3.115

A Qualitative Course-Based Inquiry into the Concept of Love as a Central Component of Child and Youth Care Practice

2019· article· en· W4244030499 on OpenAlexaff
Kylie Schneider, Melanie Dziwenka, Bobbi Schweighardt, Gerard Bellefeuille

Bibliographic record

VenueInternational Journal of Educational Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCuriosityThematic analysisPsychologyQualitative researchPedagogyComponent (thermodynamics)SociologySocial psychologySocial science

Abstract

fetched live from OpenAlex

Child and Youth Care (CYC) students have the right to be engaged in pedagogical practices that inspire and arouse their curiosity about their field of practice. Undergraduate course-based research in which students have an opportunity to conduct authentic research within a for-credit course is one such high-impact pedagogical practice with a growing body of evidence-based outcomes. This article presents an undergraduate course-based research project that examined child and youth care student‘s beliefs about displaying love as a component of their practice. Located in the constructivist/interpretive research paradigm, this course-based research project collected data through the use of an expressive arts-based data method followed by a semi-structured questionnaire. Four overarching themes were identified during the thematic analysis: (a) authentic caring involves expressions of love, (b) expressions of love are an essential component of growth and development, (c) loving care as an ethic of relational practice, and (d) but…professionalism stands in the way. The results of this course-based study suggest that expressing love as a component of relational-centred CYC practice is not fully understood by CYC students and that much more research is needed to explore this issue.

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)0.002
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 score0.185
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.044
GPT teacher head0.448
Teacher spread0.403 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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