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Record W4234186961 · doi:10.32920/ryerson.14663277

Drinking water quality and trust : communities and risk information

2021· preprint· en· W4234186961 on OpenAlexaffabout
Caitlin J Burley

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPerceptionGovernment (linguistics)Risk perceptionBusinessRisk communicationWater qualityQuality (philosophy)Water sourceEnvironmental healthEnvironmental planningPsychologyGeographyWater resource managementMedicineEnvironmental scienceRisk analysis (engineering)

Abstract

fetched live from OpenAlex

Ontario drinking water systems face increasing contamination risks due to over-taxed water systems. Canadian perceptions of drinking water, and trust in government, have declined as a result of Walkerton and other contamination episodes. Research in the field of trust, risk communication and risk perception has developed extensively in recent years. However, there is very little research regarding risk perception, communication and trust as it relates to drinking water. This study investigated drinking water perceptions, trust in drinking water authorities and communication needs of a small Ontario municipality with a positive drinking water history and good communication practices. The results indicated that the community members had positive perceptions about their source and drinking water. They had high levels of trust in their local government and low levels of trust in outside sources. Despite the high levels of trust in the local government, the residents displayed additional information needs; suggesting the presence of critical trust.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.066
GPT teacher head0.350
Teacher spread0.284 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2021
Admission routes2
Has abstractyes

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