Navigating Change: Understanding Transformations in Rural Economic Development Knowledge Translation and Transfer
Bibliographic record
Abstract
Rural economic development practitioners are essential in fostering prosperous economies and communities across Ontario. Over the past few decades, the role of these practitioners has evolved significantly. The COVID-19 pandemic has further reshaped how rural economies are organized, and how economic development professionals must adapt to support them. As a result, there is a growing need for updated information to better understand the evolving role of rural economic development practitioners, the skills and capacities they require to effectively support rural economies, and the formats in which they need to acquire this new knowledge. The purpose of this research is to understand the key skills, capacities, and knowledge sets required by economic development practitioners in rural Ontario; to understand the role and activities conducted by rural economic development practitioners to support rural prosperity, particularly in light of COVID-19; and to evaluate existing KTT activities and outputs and to identify the ideal formats for sharing knowledge with rural economic development practitioners. An online survey was conducted among economic development officers working in rural local governments across Ontario. The survey gathered valuable insights into current activities, policies, strategies, and resources aimed at supporting rural economic development. The findings reveal a clear trend of increasing responsibilities and activities, coupled with limited human and financial resources. The survey highlights key areas of knowledge necessary to support rural economies, along with the preferred formats for knowledge sharing. This analysis enhances our understanding of economic development in rural Ontario and provides insights into how to better support rural economic development practitioners.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.039 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".