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Record W6912669858 · doi:10.5281/zenodo.3775611

Census program data viewer, 2016 Census

2018· article· en· W6912669858 on OpenAlexaffabout

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsCensusGeospatial analysisData visualizationVisualizationProduct (mathematics)Presentation (obstetrics)Process (computing)Casual

Abstract

fetched live from OpenAlex

The Census Program Data Viewer (CPDV) is Statistics Canada's new web-based data visualization tool that will make statistical information more interpretable by presenting key indicators in a visual dashboard. Driven by geography and analytical indicators the CPDV allows casual users to see complex conceptual relationships with ease. The presentation will provide an overview of the product, how it works, and how it can be used by different user communities. We discuss the process of working with Goecortex Essential Technologies to refine a product to meet accessibility standards and host the large volume of data that has been made available. The effort to produce large volumes of information and integrate it with geospatial information was a considerable challenge and there are lessons learned that we would like to share. The CPDV is envisioned as a tool to allow a great number of non-sophisticated data users to easily access and interpret Census data for reference and research purposes. Feedback from the IASSIST and ACMLA communities will be invaluable to help meet this vision and provide a great experience for users.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0050.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.009

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 head0.106
GPT teacher head0.340
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2018
Admission routes2
Has abstractyes

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