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Record W6950520794 · doi:10.5683/sp3/ypotbr

CIW - Kingston, Frontenac, Lennox & Addington Community Wellbeing Survey

2013· dataset· en· W6950520794 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2013
Typedataset
Languageen
Field
Topic
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGeneral partnershipQuestionnaireIndex (typography)Survey researchPerceptionPublic healthSurvey data collection

Abstract

fetched live from OpenAlex

This survey monitors wellbeing among residents of the City of Kingston, Greater Napanee, Lennox and Addington County, and Frontenac County, located in South-Eastern Ontario. The survey is a joint initiative of the Canadian Index of Wellbeing in partnership with the Community Foundation for Kingston & Area and KFL&A Public Health. 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 dental health, emergency preparedness, and numerous socio-economic 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)0.005
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 score0.071
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.008
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.008

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 head0.071
GPT teacher head0.298
Teacher spread0.227 · 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
Published2013
Admission routes2
Has abstractyes

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