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Record W3206272718 · doi:10.1002/pra2.515

At the Margins of Epistemology: Amplifying Alternative Ways of Knowing in Library and Information Science

2021· article· en· W3206272718 on OpenAlexaff
Beth Patin, Tami Oliphant, Danielle Allard, LaVerne Gray, Rachel Ivy Clarke, Jasmina Tacheva, Kayla Lar‐Son

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSociologyEpistemologySocial epistemologyInjusticeFeminismEquity (law)Field (mathematics)Social sciencePolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

Abstract This panel argues a paradigm shift is needed in library and information science (LIS) to move the field toward information equity, inclusion, relevance, diversity, and justice. LIS has undermined knowledge systems falling outside of Western traditions. While the foundations of LIS are based on epistemological concerns, the field has neglected to treat people as epistemic agents who are embedded in cultures, social relations and identities, and knowledge systems that inform and shape their interactions with data, information, and knowledge as well as our perceptions of each other as knowers. To achieve this shift we examine epistemicide—the killing, silencing, annihilation, or devaluing of a knowledge system, epistemic injustice and a critique of the user‐centered paradigm. We present alternative epistemologies for LIS: critical consciousness, Black feminism, and design epistemology and discuss these in practice: community generated knowledges as sites of resistance and Indigenous data sovereignty and the “right to know”.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.701
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.003
Scholarly communication0.0000.007
Open science0.0000.000
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 head0.022
GPT teacher head0.272
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2021
Admission routes1
Has abstractyes

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