MétaCan
Menu
Back to cohort
Record W2399938069 · doi:10.5281/zenodo.3783436

The UNESCO Institute for Statistics: How we decide what data to collect

2003· article· en· W2399938069 on OpenAlexaff
Diane Stukel

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2003
Typearticle
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsUNESCO Institute for Statistics
Fundersnot available
KeywordsComputer scienceStatisticsData scienceMathematics

Abstract

fetched live from OpenAlex

As presented at IASSIST 2003 conference

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)0.180
metaresearch head score (Gemma)0.526
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.820
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1800.526
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0090.018
Science and technology studies0.0040.010
Scholarly communication0.0180.021
Open science0.0030.007
Research integrity0.0090.025
Insufficient payload (model declined to judge)0.0100.009

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 head0.178
GPT teacher head0.341
Teacher spread0.163 · 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 designNot applicable
DomainMethods
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

Citations0
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicCensus and Population EstimationFrench-language works237,207