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Record W2557887271 · doi:10.5206/fpq/2016.2.8

Fierce Love: What We Can Learn about Epistemic Responsibility from Histories of AIDS Advocacy

2016· article· en· W2557887271 on OpenAlexaffvenueabout
Alexis Shotwell

Bibliographic record

VenueFeminist Philosophy Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsSociologyPrivilege (computing)EpistemologyReductionismAction (physics)PoliticsContext (archaeology)Political scienceLaw

Abstract

fetched live from OpenAlex

This is the fourth paper in the invited collection. Shotwell examines the work of direct-action activists as forms of medical activism that express a non-reductionist and complex intersectional science and technology practice, bridging lay and professional medical contexts. Shotwell draws on Lorraine Code’s generative theory of the importance of “ecological thinking” as one way to practice what she calls “epistemic responsibility,” and to think about the varied and complex early responses of activists in Canada to AIDS. Activists made wide-ranging, theoretically sophisticated, and socially significant interventions in how AIDS manifested in Canada; their interventions manifested a kind of political work welcoming to unpredictable and emergent medical and social situations. Three preliminary insights are offered, from an ongoing research project investigating the history of AIDS activism in the Canadian context: (1) the usefulness of Code’s conception of the social imaginary for understanding how calcified social relations shape and limit the conditions for responsible knowing; (2) the importance of recognizing the communal and social nature of knowledge as a key piece of epistemic responsibility, articulated in terms of collective epistemic privilege; and (3) the possibility for practices of epistemic responsibility to create virtuous epistemic effects beyond what is known (about) or intended by particular agents.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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 teacher head, 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

Citations3
Published2016
Admission routes3
Has abstractyes

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