Publication Repository: The provision of drinking water in First Nations communities and Ontario municipalities: Insight into the emergence of water sharing arrangements
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".