Empirical Data Archive: Characterizing Water Services in Ontario First Nations and Municipalities 2009-2010
Bibliographic record
Abstract
Project Summary: On many First Nations’ reserves across Canada, lack of safe drinking water is a chronic problem. Many First Nations and municipalities across Ontario have formed collaborative relationships to achieve mutually beneficial outcomes, such as improved service provision, or economic development. The aim of this research project was the study of collaborative water sharing arrangements between First Nations and municipalities in Ontario. This $371,300 project took place between 2018-2025. It was an interdisciplinary effort, involving a mix of quantitative and qualitative methods including statistical analysis, and in-depth case studies. It involved the efforts of several Undergraduate and Graduate Research Assistants, in addition to the research team identified below. The central aim was to identify the potential scope for water sharing in the Province, and factors influencing communities to engage, or not engage, in these exchanges. The empirical portion of the research that produced this data was led by Brady Deaton, with data organization and archiving efforts led by Bethany Lipka. The Canadian Tri-Agency Statement of Principles on Digital Data Management states that “research data collected with the use of public funds belong, to the fullest extent possible, in the public domain and available for reuse by others”. The research team has striven to meet this open data standard, by making all empirical project data available to future users in a well-documented and accessible manner. This repository is part of a broader collection that includes additional archived resources from the above described project, including publication repositories with replication files. The complete collection can be accessed within the Collaborative Relationships Between First Nations and Municipalities project collection in the Agri-environmental Research Data Repository. Any questions regarding this data archive can be directed to the data contacts. Research Team: Dr. Brady Deaton, Primary Investigator, Department of FARE, University of Guelph Dr. Sheri Longboat, Co-Investigator, School of Environmental Design and Rural Development, University of Guelph Dr. Christopher Alcantara, Co-Investigator, Political Science, Western University Bethany Lipka, Project Coordinator, Department of FARE, University of Guelph
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".