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Record W4365140329 · doi:10.1162/posc_a_00590

Misconceptions in Science

2023· article· en· W4365140329 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

VenuePerspectives on Science · 2023
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsUniversité du Québec à Montréal
FundersFédération Wallonie-BruxellesBelgian Federal Science Policy OfficeFonds De La Recherche Scientifique - FNRSUniversité du Québec à Montréal
KeywordsVariety (cybernetics)EpistemologyRaising (metalworking)Scientific misconceptionsFocus (optics)PsychologyScience educationEngineering ethicsComputer scienceMathematics educationPhilosophyMathematicsArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

Abstract Disagreement in science exists in a variety of strengths, from doubt-raising articles and issues of non-reproducibility up to raging disputes and major controversies. An often-latent form of disagreement consists of misconceptions whereby false ideas are held that run contrary to what is commonly accepted as knowledge. Misconceptions have been the focus of much research in education science and psychology. Here we draw attention to misconceptions that may arise in the very practice of science. We highlight formal features that can be used to characterize misconceptions and distinguish them from controversies, in addition to how they relate to knowledge creation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.932
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.451
Teacher spread0.363 · 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