MétaCan
Menu
Back to cohort
Record W2046969815 · doi:10.5539/cis.v3n3p139

Validate Conference Paper using Dice Coefficient

2010· article· en· W2046969815 on OpenAlexvenueno aff
Nurhilyana Anuar, Abu Bakar Md Sultan

Bibliographic record

VenueComputer and Information Science · 2010
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFontComputer scienceDiceArtificial intelligenceMathematicsStatistics

Abstract

fetched live from OpenAlex

<!-- /* Font Definitions */ @font-face {font-family:SimSun; panose-1:2 1 6 0 3 1 1 1 1 1; mso-font-alt:??; mso-font-charset:134; mso-generic-font-family:auto; mso-font-format:other; mso-font-pitch:variable; mso-font-signature:1 135135232 16 0 262144 0;} @font-face {font-family:

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)0.010
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.328
Threshold uncertainty score0.958

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.105
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0150.008
Science and technology studies0.0030.001
Scholarly communication0.0080.005
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3280.197

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 head0.382
Teacher spread0.331 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations20
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueComputer and Information ScienceSame topicInnovative Teaching and Learning MethodsFrench-language works237,207