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SITUATING KNOWLEDGES AS COALITION WORK

2007· article· en· W2075933533 on OpenAlexaff
Maureen Ford

Bibliographic record

VenueEducational Theory · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsSituatedPerformative utteranceSociologyEmbodied cognitionEpistemologySalientPoliticsSelection (genetic algorithm)PublicsOrder (exchange)AestheticsPhilosophyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract In this essay Maureen Ford examines a selection of situated knowledges discourses in order to make explicit their attention to political effects. She contends, first, that the “epistemic public(s)” constituted through these discourses are multiple, interactive, performative, and layered, and further that they are explicitly political in ways that are denied by standard epistemological approaches. Furthermore, Ford maintains that the political effects circulated within standard and situated knowledges are epistemologically and educationally significant. Attending to the work of Donna Haraway, Patricia Hill Collins, and María Lugones, she teases out some of the various strategies through which their texts explicitly invoke politically salient, multidimensional, embodied engagement with spaces, people, and discourses in order to make sense. Ford explores the ramifications for educators and educational theorists of addressing such epistemic publics, noting that they are complex and almost inevitably uncomfortable. Taking up discourses of situated knowledges, she suggests, proliferates the avenues through which educators and educational theorists can contribute to the creation and contestation of “public” truths.

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)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0120.054
Scholarly communication0.0160.026
Open science0.0030.020
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.034
GPT teacher head0.395
Teacher spread0.361 · 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.

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

Citations8
Published2007
Admission routes1
Has abstractyes

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