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Record W6886022649 · doi:10.14288/1.0388704

2018 Statistics Canada – Canadian Housing Survey 46-10-0024-01: Dwelling and neighbourhood satisfaction, by tenure including social and affordable housing and structural type of dwelling

2020· dataset· en· W6886022649 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Collections · 2020
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCensusNeighbourhood (mathematics)PopulationMetropolitan areaHousing tenureAmerican Community SurveyTable (database)

Abstract

fetched live from OpenAlex

This dataset includes Statistics Canada table 46-10-0024-01, titled “Dwelling and neighbourhood satisfaction, by tenure including social and affordable housing and structural type of dwelling”. The table includes information on satisfaction with housing by tenure, condominium status, and structural type of dwelling. One of the tenure categories is renters in social and affordable housing. The table has been edited to include only geographies from British Columbia. The table is available in CSV and Excel Workbook format. Definitions and notes are included at the bottom of the spreadsheet. This data set was collected as part of the Canadian Housing Survey by Statistics Canada. Geographies: British Columbia, Large urban population centres, British Columbia, Medium population centres, British Columbia, Small population centres, British Columbia, Rural areas, British Columbia, Vancouver, British Columbia, Other census metropolitan areas, British Columbia, Census agglomerations, British Columbia

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)0.001
metaresearch head score (Gemma)0.010
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 score0.072
Threshold uncertainty score0.278

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.023
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0720.035

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 head0.048
GPT teacher head0.293
Teacher spread0.245 · 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
Published2020
Admission routes1
Has abstractyes

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