Enhancing Ontario’s Rural Infrastructure Preparedness: Inter-Community Service Sharing in a Changing Climate — Interim Report 2: Provincial Survey Results
Bibliographic record
Abstract
Rural communities draw from their history of doing more with less, strong social networks and an intimate relationship with the natural environment to achieve economic innovation, positive social capacity development and environmental sustainability. These spaces also experience challenges including preparing for the impacts of climate change (CC). Ontario has already felt some of these effects leading to millions of dollars of damage to the province’s infrastructure. Exacerbated by an aging infrastructure built by now outdated assumptions, the vulnerability to CC will likely increase and the built-in coping range may not be adequate to handle future climate extremes.\nThe purpose of the broader research project is to 1) assess the potential of inter-community service cooperation (ICSC) as a possible tool to address the impacts of CC in small (500-7500 pop.) Ontario rural communities south of the Sudbury region and 2) understand the extent to which such collaboration and the impacts of CC are, or could be, embedded within the community’s infrastructure (asset) management processes (AMP). For the purposes of this project, rural communities include all Ontario communities who self-identify as rural, or partially rural, and have membership in the Rural Ontario Municipal Association (ROMA). This project is guided by a Project Advisory Board (PAB) consisting of experts representing key rural sectors. The research is focused on the infrastructure sectors most likely affected by CC, that are under the control of Ontario rural communities, and where ICSC shows promise.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".