MétaCan
Menu
Back to cohort
Record W2102599529 · doi:10.1037/a0034177

Why the resistance to statistical innovations? Bridging the communication gap.

2013· article· en· W2102599529 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.

Bibliographic record

VenuePsychological Methods · 2013
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsBridging (networking)Bridge (graph theory)Resistance (ecology)UnavailabilityPublicationComputer scienceMandateStatistical analysisStatistical hypothesis testingStatistical softwarePublish or perishManagement sciencePsychologyData scienceStatisticsPublishingEngineeringPolitical scienceMathematicsComputer security

Abstract

fetched live from OpenAlex

While quantitative methodologists advance statistical theory and refine statistical methods, substantive researchers resist adopting many of these statistical innovations. Traditional explanations for this resistance are reviewed, specifically a lack of awareness of statistical developments, the failure of journal editors to mandate change, publish or perish pressures, the unavailability of user friendly software, inadequate education in statistics, and psychological factors. Resistance is reconsidered in light of the complexity of modern statistical methods and a communication gap between substantive researchers and quantitative methodologists. The concept of a Maven is introduced as a means to bridge the communication gap. On the basis of this review and reconsideration, recommendations are made to improve communication of statistical innovations.

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)0.005
metaresearch head score (Gemma)0.033
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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: Methods · Consensus signal: Methods
Teacher disagreement score0.381
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.468
GPT teacher head0.582
Teacher spread0.114 · 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