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Record W4393753614 · doi:10.5281/zenodo.3635095

Testing ritual knot tracing for cognitive priming effects rules out analytic analogy: Core Data Sets

2019· dataset· en· W4393753614 on OpenAlexaffabout
Zahra Vahedi, Sari Park, Jamin Pelkey, Stéphanie Walsh Matthews

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnalogyKnot (papermaking)TracingPriming (agriculture)Core (optical fiber)CognitionComputer scienceCognitive sciencePsychologyEpistemologyPhilosophyProgramming languageEngineeringNeuroscienceBiology

Abstract

fetched live from OpenAlex

Core data sets analyzed for Studies 1 and 2 in "Testing ritual knot tracing for cognitive priming effects rules out analytic analogy". Note: In keeping with Ryerson University Research Ethics Board protocol #REB 2017-065, data sets are fully anonymized, revealing coded values only and removing all personal information and metadata peripheral to the main study (including reported age, gender, language proficiency, and language usage coding). See main paper and supporting materials for discussion of measures, parameters, conditions, and variables. Corresponding author contact: jpelkey@ryerson.ca

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)0.000
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0050.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.238
GPT teacher head0.403
Teacher spread0.165 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2019
Admission routes2
Has abstractyes

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