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Record W4392985939 · doi:10.3138/ecf.36.2.315

A Collaborative Approach to Antiracist Pedagogy

2024· article· en· W4392985939 on OpenAlexvenueno aff
Nicole Hamblin, Carmen Faye Mathes

Bibliographic record

VenueEighteenth-Century Fiction · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyPedagogy

Abstract

fetched live from OpenAlex

This cowritten Reflections essay describes an upper-division seminar for honours and master’s students that brought together established antiracist pedagogical principles (for example, decentering Britain) with a commitment to hands-on, in-class collaboration. This collaborative dimension buoyed the class’s capacity to encounter both the challenges and insights that the antiracist framework brought to the fore. In a course that sought to engage with Romantic poetry of “labour and longing” in the age of transatlantic slavery, working together became both an object of study and a learning practice. The coauthored nature of this essay therefore advances, beyond the end of term, the importance of listening and learning from one another in the pursuit of more inclusive, wide-ranging, and (very often) discomforting learning environments, even as such environments can only ever be imperfect.

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)0.013
metaresearch head score (Gemma)0.014
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: none
Teacher disagreement score0.016
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0160.035
Scholarly communication0.0130.011
Open science0.0030.021
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0100.002

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 head0.020
GPT teacher head0.354
Teacher spread0.334 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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