Building the Future: Rural Infrastructure and Regional Economic Development
Bibliographic record
Abstract
Communities of all sizes must balance fiscal realities, changing economies, aging infrastructure, changing demographics, and a challenging climate as they work to manage their core infrastructure assets and accommodate and/or address new infrastructure and service demands. Given these challenges, are rural Ontario communities capable of responding to infrastructure pressures and opportunities? How does that capacity – or lack thereof – affect a community’s current and future long-term economic development? Funded by the Ontario Ministry of Agriculture, Food and Rural Affairs through the University of Guelph-OMAFRA Research Partnership, this research initiative will examine the capacity of different communities in rural Ontario to respond to infrastructure pressures and how these response impact their short and long-term economic well-being. Running from 2018-2021, the research team will use surveys, workshops, content analysis, and case studies, to develop recommendations for addressing these issues through both immediate and long-term policy alternatives. This research initiative will directly support rural Ontario’s economic vitality by providing three key benefits: enhanced understanding of the diversity and varying levels of rural community capacities, improved and more nuanced public policy, and enhanced rural infrastructure development programming.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".