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Record W2853473891

Re-imagining care: thinking with feminist ethics of care

2018· dissertation· en· W2853473891 on OpenAlexaboutno aff
Jenny Thomson

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicFeminism, Gender, and Sexuality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthics of careFeminist ethicsSociologyEngineering ethicsGender studiesEnvironmental ethicsEpistemologyPhilosophyEngineering
DOInot available

Abstract

fetched live from OpenAlex

The term care has been part of the CYC title since the University of Victoria School of Child and Youth Care (CYC) opened in the 1970’s, making care a central aspect of CYC’s public and professional identity. The purpose of this research is to explore how care is conceptualized in Foundations of Child and Youth Care Practice; a Canadian textbook widely used in CYC postsecondary education programs. This text introduces future CYC practitioners to important aspects of CYC praxis, such as care. In this research I use the Trace method developed by Selma Sevenhuijsen (2004) to analyze the text. In this analysis, feminist ethics of care acts both as a lens for analyzing care and as a framework for renewing ways of thinking about and doing care in CYC. Key findings show that conceptualizations of care in the text are deeply influenced by neoliberal ‘justice’ frameworks leading to care being framed as always ‘good’ and understood as apolitical, simple and instrumental. This reveals a lack of theorizing about care in the text and suggests that understandings of care are taken for granted and devalued. These conceptualizations of care cannot account for the complexities of the care relationship and do not adequately reflect the lived experience of young people and families. This research advocates for engagement with feminist ethics of care as a starting point for re-imagining care in CYC and offers suggestions for what this might look like.

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)0.002
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: none
Teacher disagreement score0.535
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.055
GPT teacher head0.359
Teacher spread0.304 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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