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Record W2302658167 · doi:10.52041/srap.03111

Statistics in the classroom learning to understand societal issues

2003· article· en· W2302658167 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsOutreachThe InternetComputer scienceRelevance (law)Resource (disambiguation)Statistics educationStatistical analysisOfficial statisticsMathematics educationWorld Wide WebStatisticsPublic relationsData sciencePsychologyPolitical scienceMathematics

Abstract

fetched live from OpenAlex

The role of a National Statistical Office is to produce the official statistics for its country and to help citizens understand the issues underlying the society and economy. Reams of statistical tables cannot do the job alone: to become ‘information’, statistics must be analyzed and portrayed effectively for the target audience, i.e. citizens of all ages. Students (high school and university) can most easily be reached through the Internet. Not only has Internet access the highest rate among the young, but a website is also very cost-effective to make material available in formats and in quantities which would not have been possible in the paper age. The availability of such information, however, must be promoted to students and teachers and efforts must be made to show them the relevance of the material for the classroom. This paper will describe the activities, their results, and lessons learned from the statistical learning resource and education outreach programs in Statistics Canada.

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)0.007
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 score0.125
Threshold uncertainty score0.866

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 head0.285
GPT teacher head0.477
Teacher spread0.192 · 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

Quick stats

Citations5
Published2003
Admission routes2
Has abstractyes

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