MétaCan
Menu
Back to cohort
Record W6931998574 · doi:10.5683/sp2/tthjvn

Publication Repository: The provision of drinking water in First Nations communities and Ontario municipalities: Insight into the emergence of water sharing arrangements

2021· dataset· en· W6931998574 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2021
Typedataset
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWater supplyPopulationInformation sharingKey (lock)Potable waterDeveloping country

Abstract

fetched live from OpenAlex

This data set characterizes potable water supply in the province of Ontario in the years 2009-2010. It includes 419 communities: 118 First Nations communities and 301 municipalities. It identifies communities that were supplied in whole or in part through water sharing arrangements (WSAs) during the study period. And it includes a number of key community characteristics: northerness, elevation, population density, remoteness, and regional wealth. This data set was gathered for the purpose of exploring factors influencing local communities in Ontario - First Nations and municipalities - to participate in WSAs. Specifically, in our paper we explore whether First Nations communities – many of which suffer persistently poor drinking water conditions – are less likely to be engaged in WSAs than municipalities. Our findings suggest that while First Nations have a lower rate of WSA particpation compared to municipalities, the likelihood of WSA participation is more strongly influenced by key community characteristics like geography, remoteness and regional wealth. A STATA .do file has been included that contains the commands used in our analysis, for ease of replication. This paper is part of a broader research project on collaborative relationships between First Nations and municipalities in Ontario. For more information on this project, visit the Collaborative Relationships Between First Nations and Municipalities in Ontario project website.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.386

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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 designNot applicable
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
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueBorealisSame topicChemotherapy-induced organ toxicity mitigationFrench-language works237,207