Informing rural municipal sustainability: A case study analysis of rural communities
Bibliographic record
Abstract
This thesis investigates communication of information as a basis for facilitating rural community sustainability. Rural communities face complex global and local challenges to their social, economic, and environmental sustainability. Globalization issues directly impact all aspects of rural community sustainability. Increased urbanization and centralization of services also impacts on socio-economic characteristics of community and rural environmental quality (Bryden, 1994). Southern Ontario rural communities confront these challenges in spite of the productive farmlands and an abundant resource base that characterize that Province. Issues complexity is a barrier to community sustainability (UNCED, 1992; Mitchell, 1994). Global access to local resources combined with Provincial economic reforms reshape socio/cultural profiles of communities. Innovative methods are required to facilitate more balanced, sustainable approaches to community change. Information is required as a basis for informed decision-making and improved prospects for a sustainable future (WCED, 1987; Lyle, 1994, Wackemagle and Rees, 1996). This thesis explores these issues in the context of a research program related to sustainable rural communities focused on information and communication between rural community stakeholders and decision-makers. Drawing from current and emerging theory, a model for analysis of rural community sustainability (FARMS) incorporating publicly-derived sustainable indicators, has been tested using case study sites and focus group data gathering techniques, and is proposed as a starting point towards evolving tools for facilitating community sustainability. Conclusions are drawn and recommendations proposed based on the testing of FARMS. Future research needs and directions are identified.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".