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Record W6932012323 · doi:10.5683/sp3/uekdc5

CIW - Community Wellbeing Survey - Wood Buffalo

2016· dataset· en· W6932012323 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2016
Typedataset
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeneral partnershipSurvey data collectionProsperityGeneral Social SurveyQuestionnaireWell-beingSurvey methodology

Abstract

fetched live from OpenAlex

This survey monitors wellbeing among residents of the Regional Municipality of Wood Buffalo, located in northern Alberta. The survey is a joint initiative of the Canadian Index of Wellbeing in partnership with Social Prosperity Wood Buffalo. These data represent the “Look into Wood Buffalo” Community Wellbeing Survey (N=554), which includes For McMurray, and several outlying communities such as Anzac, Conklin, Fort Chippewyan, and Saprae Creek. The primary objectives of this survey are to (a) gather data on the wellbeing of residents which could be monitored over time; and, (b) to provide information on specific aspects of wellbeing that could be used to inform policy issues and community action. The purpose of the survey is to better understand subjective perceptions of wellbeing of residents in the survey area. The survey provides information based on eight domains of wellbeing, as identified by the Canadian Index of Wellbeing: Community Vitality, Democratic Engagement, Environment, Education, Healthy Populations, Leisure and Culture, Living Standards, and Time Use. The questionnaire collected additional information about numerous socio-economic and household characteristics.

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)0.004
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 score0.236
Threshold uncertainty score0.475

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.007

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 head0.047
GPT teacher head0.314
Teacher spread0.267 · 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
Published2016
Admission routes2
Has abstractyes

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