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Record W6913249799 · doi:10.5683/sp3/q56lyo

Health Regions: Boundaries and correspondence with census geography, 2005 [Canada]

2005· dataset· en· W6913249799 on OpenAlexaboutno aff

Bibliographic record

VenueBorealis · 2005
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCensusProduct (mathematics)Health careHealth geographyPublic healthHealth policyDocumentationPopulation healthHealth equity

Abstract

fetched live from OpenAlex

Health Regions 2005 describes in detail the health region limits as of June 2005 and their correspondence with the 1996 and 2001 Census geography. Health regions are defined by the provinces and represent administrative areas or regions of interest to health authorities. This product contains correspondence files (linking health regions to 2001 Census geographic codes) and digital boundary files. User documentation provides an overview of health regions, sources, methods, limitations and product description (file format and layout).In addition to the geographic files, this product also includes 2001 Census data (basic profile) for health regions. A result of the co-operation of provincial health ministries, Alberta Treasury and BC Stats, Health Regions 2005 is part of the Health Information Roadmap initiative, a joint effort among the Canadian Institute for Health Information, Health Canada and Statistics Canada. Health Regions 2005 was produced by the Health Statistics Division in collaboration with the Geography and Dissemination divisionsHealth regions are definedby provincial governments as the areas of responsibility for regional healthboards (i.e., legislated) or as regions of interest to health care authorities. This product replaces Health Regions 2000. For current Health Regions data, refer to 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.

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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.098
Threshold uncertainty score0.371

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.036
Science and technology studies0.0040.001
Scholarly communication0.0070.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0980.039

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 head0.013
GPT teacher head0.260
Teacher spread0.246 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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
Published2005
Admission routes1
Has abstractyes

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