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Risking Attachments in Teaching Child and Youth Care in Twenty-First-Century Settler Colonial, Environmental and Biotechnological Worlds

2017· article· en· W2764167933 on OpenAlexaff
Jennifer White, Scott Kouri, Veronica Pacini‐Ketchabaw

Bibliographic record

VenueInternational Journal of Social Pedagogy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHarmColonialismPoliticsCapitalismSociologyService (business)CriminologyWork (physics)Youth workChild carePolitical scienceGender studiesPublic relationsNursingLawMedicineEngineeringBusiness

Abstract

fetched live from OpenAlex

As a way to implicate ourselves in the politics of teaching child and youth care, we write as witnesses of the world and, in so doing, we make risky attachments by exploring a politically engaged child and youth care education that does not promote insurance, control or detachment. Rather, in this paper we critically locate child and youth care education within the political and economic realities of today’s world. We grapple with the complexities of educating child and youth care practitioners deeply embedded in neoliberal capitalism and settler colonialism, and explore the conceptual shifts that we are experimenting with in our own teaching practices to engage in human service work that responds with care to individual and family need and suffering by engaging with the very structures that perpetuate harm and violence in our society.

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)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0220.046
Scholarly communication0.0110.005
Open science0.0010.018
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.368
Teacher spread0.344 · 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 designNot applicable
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

Citations9
Published2017
Admission routes1
Has abstractyes

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