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Record W2329258959 · doi:10.1215/22011919-3616380

Unruly Raccoons and Troubled Educators: Nature/Culture Divides in a Childcare Centre

2016· article· en· W2329258959 on OpenAlexaff
Veronica Pacini-Ketchabaw, Fikile Nxumalo

Bibliographic record

VenueEnvironmental Humanities · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Victoria
FundersUniversity of Texas at Austin
KeywordsApprehensionImpossibilitySituatedFutures contractSociologyIndigenousEpistemologyPolitical scienceEcologyPhilosophyLaw

Abstract

fetched live from OpenAlex

Abstract Current times of anthropogenically damaged landscapes call us to re-think human and nonhuman relations and consider multiple possibilities for alternative and more sustainable futures. As many environmental and Indigenous humanities scholars have noted, central to this re-thinking is unsettling the colonial nature/culture divide in Western epistemology. In this article, through a series of situated, small, everyday stories from childcare centres, we relate raccoon-child-educator encounters in order to consider how raccoons' repeated boundary-crossing and their apprehension as unruly subjects might reveal the impossibility of the nature/culture divide. We tell these stories, not to offer a final fixed solution to the asymmetrical, awkward and frictional entanglements of humans' and raccoons' lives, but as a responsive telling that may bring forth new possibilities for responsible, affective and ethical co-habitations.

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)0.005
metaresearch head score (Gemma)0.010
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 score0.037
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0370.048
Scholarly communication0.0130.007
Open science0.0020.016
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.000

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 head0.009
GPT teacher head0.252
Teacher spread0.242 · 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

Citations92
Published2016
Admission routes1
Has abstractyes

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