MétaCan
Menu
Back to cohort
Record W4236940398 · doi:10.4324/9781351186674-10

Learning with children, ants, and worms in the Anthropocene: towards a common world pedagogy of multispecies vulnerability

2020· book-chapter· en· W4236940398 on OpenAlexaboutno aff
Affrica Taylor, Veronica Pacini-Ketchabaw

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAnthropoceneVulnerability (computing)Environmental ethicsGeographyAstrobiologyPsychologySociologyBiologyComputer sciencePhilosophyComputer security

Abstract

fetched live from OpenAlex

This article takes the naming of the Anthropocene as a moment of pedagogical opportunity in which we might decentre the human as the sole learning subject and explore the possibilities of interspecies learning. Picking up on current Anthropocene debates within the feminist environmental humanities, it considers how educators might pedagogically engage with the issue of intergenerational environmental justice from the earliest years of learning. Drawing on two multispecies ethnographies within the authors’ Common World Childhoods’ Research Collective, the article describes some encounters among young children, worms and ants in Australia and Canada. It uses these encounters to illustrate how paying close attention to our mortal entanglements and vulnerabilities with other species, no matter how small, can help us to learn with other species and rethink our place in the world.

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)0.002
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.016
Scholarly communication0.0050.007
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.037
GPT teacher head0.347
Teacher spread0.310 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicGeographies of human-animal interactionsFrench-language works237,207