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Record W4400741196 · doi:10.1080/14733285.2024.2371000

From ‘No’ to ‘Know’: a heuristic for decolonizing research with youth

2024· article· en· W4400741196 on OpenAlexfundno aff
Joanna Kocsis

Bibliographic record

VenueChildren s Geographies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaInternational Development Research Centre
KeywordsHeuristicNeed to knowSociologyComputer scienceArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

This paper addresses epistemic violence in social science research, drawing on a multiyear study with marginalized teenagers in Old Havana, Cuba to articulate an onto-epistemological approach to knowledge production that can contribute to the decoloniality of knowledge production. Building on decolonial, feminist, Indigenous, and poststructuralist theories, the heuristic presented here contributes an alternative to conventional positivist understandings of knowledge, by defining knowledge as social, created, performed and resistant, and illustrates how these theoretical tenets can be made material in research practice, in this case through the use of arts-based methods. Responding to calls to decolonize knowledge within the field of children’s geographies and adjacent disciplines, this paper addresses the attendant need to reconceptualize what counts as knowledge and identify methodological innovations to support the achievement of these changes.

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)0.061
metaresearch head score (Gemma)0.054
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.321

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0160.123
Scholarly communication0.0130.021
Open science0.0040.017
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.381
Teacher spread0.330 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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