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Record W2888552095 · doi:10.18357/jcs.v43i1.18264

With(in) the Forest: (Re)conceptualizing Pedagogies of Care

2018· article· en· W2888552095 on OpenAlexvenueno aff
H. Charles Woods, Narda Nelson, Sherri-Lynn Yazbeck, Ildikó Danis, Deanna B. Elliott, Julia Watters Wilson, Johanna Payjack, Anne Pickup

Bibliographic record

VenueJournal of Childhood Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsnot available
Fundersnot available
KeywordsForegroundingEarly childhood educationPoliticsEarly childhoodSociologyStewardship (theology)Materiality (auditing)ColonialismEnvironmental ethicsPedagogyAestheticsPolitical sciencePsychologyDevelopmental psychologyArtLaw

Abstract

fetched live from OpenAlex

Drawing on moments from an early learning forest inquiry located on Songhees, Esquimalt, and WSÁNEĆ territories, otherwise known as Victoria, BC, this paper engages with the messy politics of “care” that emerge when early childhood education and colonized forest ecologies meet. In it, we take up the challenge of unsettling our deeply held conceptualizations of care through a series of pedagogical stumblings with young children’s worldly forest relations. Foregrounding the question “what constitutes good care in troubling times?” this discussion explores the logics we draw on to respond to the increasing sense of urgency in contemporary calls to teach children how to care for the earth. Can we learn to inhabit pedagogies of care in early childhood educational practice beyond simply retooling the extractive settler-colonial stewardship frameworks that brought us to this era of uncertainty? And what happens if we invite a wider cast of participants into our understandings of care than those prevailing early learning approaches tend to promote?

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)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.109
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0140.075
Scholarly communication0.0110.012
Open science0.0020.008
Research integrity0.0030.008
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 head0.081
GPT teacher head0.401
Teacher spread0.320 · 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 designTheoretical or conceptual
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

Citations18
Published2018
Admission routes1
Has abstractyes

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