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Record W4410100183 · doi:10.1177/18747655251335765

Characterizing “data” for official statistics

2025· article· en· W4410100183 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

VenueStatistical Journal of the IAOS · 2025
Typearticle
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOfficial statisticsStatisticsComputer scienceMathematics

Abstract

fetched live from OpenAlex

With the dramatic and rapidly growing role of “data” in contemporary societies, there is increasing interest in how best to reflect this reality in countries’ official statistics. This paper suggest that an appropriately broad concept of “data” is essential, one that includes data flows as well as data bases . We motivate this approach for the recognition in official statistics of the value and importance of data in two parts. First is the need for evidence to support two major policy area, privacy and health, and then second by two more general concerns for which National Statistical Offices (NSOs) devote major efforts, inflation and entertainment. The main conclusion is that NSOs' efforts should focus on creating microdata “portraits” not only of data bases, but also the data flows among them. As a corollary, we argue that current efforts to include “data” under the rubric of digitalization in the System of National Accounts as a monetized capital stock is too limiting and arbitrary to be useful.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.417
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

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

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 head0.085
GPT teacher head0.402
Teacher spread0.318 · 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