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Record W7019166920

Exploring Care: A Constructivist Grounded Theory Study of Women’s Experiences of Caring as a Mother and as an Early Childhood Professional in Multiple Contexts

2022· dissertation· en· W7019166920 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningConstructivist grounded theoryGrounded theoryEarly childhoodEarly childhood educationQualitative researchChild careSocial constructivism
DOInot available

Abstract

fetched live from OpenAlex

This constructivist grounded theory study explores performances of care in the home and the workplace of 28 women who identify both as mothers and as Early Childhood Professionals (ECP) in Ontario. Examining these women’s lives is valuable to study in order to interrogate how dual roles of mother-ECP influence the decisions and care practices they enact. Semi-structured, in-depth interviews were used to answer the primary research question, “How do women who identify as mothers within the Early Childhood Education and Care field navigate the performance of care in personal and professional contexts and at their intersection?” Analysis of the findings showed that women underwent a transformative experience of expanding understanding when they increased the number of roles they occupied from one (parent only or ECP only) to two (mother-ECP). Mother-ECPs defined care quintessentially as meeting needs and explained that their role in the care relationship is best characterized as putting others first. Implications of the present study for expanding pre-service ECEC training, as well as limitations and suggestions for future research, are discussed.

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)0.015
metaresearch head score (Gemma)0.009
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 score0.152
Threshold uncertainty score0.302

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0160.024
Scholarly communication0.0080.005
Open science0.0020.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.270
Teacher spread0.245 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Atrium (University of Guelph)→Same topicEarly Childhood Education and Development→French-language works237,207→