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Record W3122440281

Do Water Service Provision Contracts with Neighbouring Population Centres Reduce Drinking Water Risk on Canadian Reserves

2014· article· en· W3122440281 on OpenAlexaffabout
Bethany Woods, Brady J. Deaton

Bibliographic record

Venue2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsProbit modelPopulationIncentiveWater industryBusinessProbitWater supplyGeographyEconomicsEnvironmental scienceEnvironmental healthEnvironmental engineeringEconometrics
DOInot available

Abstract

fetched live from OpenAlex

As of 2011, 39% of drinking water systems on Canadian First Nations’ reserves could be classified as high risk, or unequipped to safely deal with the infiltration of a pollutant (Neegan Burnside 2011a). In recent years, some First Nations have contracted water services from neighboring population centres through ‘Municipal Type Agreements’, or ‘MTAs’. Using a unique data set of 804 First Nation water systems, we explore both factors that influence participation in MTAs, and the effect of participation on the likelihood that a First Nation will be under a boil water advisory. Our empirical analysis consists of two probit models. The first model describes the likelihood that a MTA agreement will emerge between a First Nation and neighbouring population centre. The second estimates the likelihood that a First Nation will be under a boil water advisory. Our primary finding is that MTAs reduce the likelihood of a boil water advisory being in effect on a reserve. This is an important consideration when developing incentives or institutions that influence infrastructure collaboration between First Nations and Canadian population centres.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.007

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 head0.022
GPT teacher head0.203
Teacher spread0.181 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2014
Admission routes2
Has abstractyes

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