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Record W4213453208 · doi:10.1177/1525822x211070463

Combining Conceptual Frameworks on Maternal Health in Indigenous Communities—Fuzzy Cognitive Mapping Using Participant and Operator-independent Weighting

2022· article· en· W4213453208 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueField Methods · 2022
Typearticle
Languageen
FieldComputer Science
TopicCognitive Science and Mapping
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchFundación CeiBAMcGill University
KeywordsWeightingConceptualizationOperator (biology)Fuzzy cognitive mapFuzzy logicComputer scienceCognitionCognitive mapKnowledge managementIndigenousManagement scienceArtificial intelligencePsychologyCognitive psychologyFuzzy setFuzzy classificationEngineeringMedicine

Abstract

fetched live from OpenAlex

A recurring issue in intercultural research is whose knowledge informs conceptualization and design of projects or interventions. Fuzzy cognitive mapping uses arrows and weights to represent stakeholder knowledge on causal relationships and can generate composite theories to inform research and action. Cognitive mapping is accessible across different cultures, but participant weighting is not always straightforward. We describe a procedure to combine and condense maps from different stakeholders and an alternative operator-independent weighting procedure adapted from Harris’s discourse analysis.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.132
GPT teacher head0.401
Teacher spread0.270 · 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