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Record W3094201508 · doi:10.18357/jcs00019910

Researching the Moral Experiences of Young Children: A Pilot Study

2020· article· en· W3094201508 on OpenAlexaffvenue
Nora Makansi, Franco A. Carnevale

Bibliographic record

VenueJournal of Childhood Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité LavalMontreal Children's HospitalMcGill UniversityDouglas Mental Health University InstituteMcGill University Health CentreUniversité de Sherbrooke
Fundersnot available
KeywordsThematic analysisData collectionParticipant observationContext (archaeology)PsychologyDevelopmental psychologyQualitative researchPedagogySociologySocial scienceGeography

Abstract

fetched live from OpenAlex

The aim of this pilot study was to develop a research design and refine data collection and analysis methods to examine moral experiences of children in an education context. We piloted two data collection methods: participant observation and one-on-one interviews in preschool classrooms and with school-aged children, respectively. Our thematic analysis revealed how children coconstruct their daily experiences in this particular context; when and how they resist rules; and what moral experiences may look like in preschool interactions and how they may be understood and expressed by school-aged children. We also discussed methodological reflections on rapport building and power dynamics within these research methods.

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)0.012
metaresearch head score (Gemma)0.012
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: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0060.004
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.118
GPT teacher head0.383
Teacher spread0.265 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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