Keynote Panel Discussion - Rural Entrepreneurship, Innovation, and Economic Development
Bibliographic record
Abstract
Keynote panel discussion with Heather Hall, Tjaart Van den Berg, and Terry Van Horn. A brief biography of each speaker is listed below Heather Hall is an Assistant Professor in the Economic Development and Innovation program in the School of Environment, Enterprise and Development at the University of Waterloo. Heather grew up in Northern Ontario and has a professional and personal interest in researching planning and economic development in northern regions. This includes: regional development planning, policy and practice; community readiness and community impacts related to natural resource development; and innovation and entrepreneurship in rural and northern contexts. Heather is the co-lead of the University of the Arctic’s thematic network on the Commercialization of Science and Technology for the North and she is the co-author of the newest edition of Planning Canadian Regions. Her research has been recognized nationally and internationally by the Canadian Association of Geographers’ Ontario division (CAGONT), the Innovation Systems Research Network (ISRN), the Regional Studies Association and she is an alumnus of the Arctic Frontiers emerging leaders program. Tjaart Van den Berg is CEO of LandInfo Technologies Inc. He has a Masters degree in Urban and Regional Planning with 30 years international experience in the development and implementation of GIS in the public and private sector. Tjaart was born and educated in South Africa and has been working with local governments in Canada for more than 20 years. In the course of his work, he has become an expert in developing and applying GIS technologies to support municipal infrastructure and services. Tjaart is passionate about assisting municipalities to be more effective in what they do. In recent years, he focused his work on designing solutions to help smaller municipalities with asset management planning needs and implementation. In addition to assisting numerous municipal and private sector clients to implement GIS and Asset Management systems in Canada, the USA and South Africa, Tjaart actively promotes the awareness of geospatial technologies. He is a regular speaker at universities and professional institutions and has been working closely with various research institutions to develop innovative technology and procedures that improve the use of GIS and Asset Management. Terry Van Horn and her family moved to Trail, BC in 2001. Using her chemical sciences background, her experience in working in industry towns of Mackenzie and Prince George, as well as her business diploma Terry joined the Kootenay Association for Science and Technology (KAST) in 2004. During her 8 years at KAST, she learned that collaboration, team work, and relationship building are necessities in rural communities where selling the viability of an idea and a region is fundamental to success. Further expanding on her skills and knowledge in 2012 Terry joined the Lower Columbia Initiatives Corporation (LCIC). As the regional Economic Development Officer she continues to promote opportunities and drive economic and business growth through strong partnerships and a passion like no other. Terry and her family are grateful for their life in this beautiful region and plan to live a long and healthy life playing golf close to their children and, hopefully one day, grand children. Video in English: https://www.youtube.com/watch?v=nK4b07xfflQ. Vidéo en français: https://www.youtube.com/watch?v=4K3I0_uTLDI
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.157 | 0.027 |
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".