MétaCan
Menu
Back to cohort
Record W6894280184 · doi:10.5683/sp3/ixrpq9

Households and the Environment Survey, 2015 [Canada]

2015· dataset· en· W6894280184 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsSustainabilityContext (archaeology)Greenhouse gasEnvironmental qualityAir quality indexSurvey data collectionEnvironmental impact assessmentClimate changeQuality (philosophy)

Abstract

fetched live from OpenAlex

<p>The Households and the Environment Survey (HES) was conducted from October 2015 to January 2016 as a supplement to the Canadian Community Health Survey. The survey was designed to specifically address the needs of its funding source the Canadian Environmental Sustainability Indicators (CESI) project, a joint venture between Statistics Canada, Environment Canada and Health Canada. The CESI project reports annually on air quality, water quality and greenhouse gas (GHG) emissions in Canada using indicators to identify areas of importance to Canadians and monitor progress. </p> <p>The HES was first conducted in 1991, 1994 and more recently in 2006, 2007, 2009, 2011 and 2013. The 2015 survey offers an expanded view on household behaviours that relate to the environment but allows for comparisons with the 1994 survey for some indicators and most of the indicators from the 2006-2013 surveys. The objective of the survey is to provide context to scientific measures of air and water quality, and greenhouse gas emissions, by gaining a better understanding of household behaviour and practices with respect to the environment. Since the HES was first conducted in 1991, environmental priorities and concerns have changed for Canadians.</p> <p>Changes in environmental practices and behaviours are reflective of these growing concerns. In order to gauge these changes, the HES measures some of the same environmental variables that were measured by the HES in previous cycles; however other environmental practices have been measured as well.</p>

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.245
Teacher spread0.214 · 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 teacher head, not a consensus.

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

Citations1
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBorealisFrench-language works237,207