Social Media for Enhancing Innovation in Agri-food and Rural Development: Current Dynamics in Ontario, Canada
Bibliographic record
Abstract
Communication for innovation in agriculture and rural development involves interactive and multi-stakeholder approaches that mobilize ideas and resources from the public and private sectors as well as civil society. Digital tools broadly referred to as Web 2.0 technologies, and in particular, social media such as Facebook, Twitter, blogs and webinars are allegedly channels of communication for innovation. These tools potentially offer support for collective learning processes and co-creation of knowledge. There is little evidence, however, to substantiate that new media are enabling innovation by and among stakeholders of agri-food and rural systems. Are diverse agri-food producers, rural entrepreneurs, scientists or researchers, community-level volunteers and public servants interacting more effectively in Web 2.0 environments? Are social media reinventing agri-food and rural information flows? Employing methods of multiple database searches, review of literature, and content analysis of 50 relevant online communities this paper identifies emerging issues in the development and use of social media in the agri-food and rural sectors with an emphasis on data from Ontario and, to a lesser extent, elsewhere in Canada. Findings suggest that the uptake of social media is still in an early, exploratory phase associated with modest opportunities and relevant limitations of Web 2.0 mediated multi-stakeholder collaboration. Notably, there are gaps in giving and receiving feedback which are intrinsic to dyadic communication as well as innovation processes. Limitations identified include (a) conflicting perceptions among stakeholders about the use, risk, credibility and institutional incentives associated with social media, and (b) lack of capacity that enables use and development of appropriate social media applications. The paper concludes by summarizing the importance of autonomous, user-oriented applications of Web 2.0 tools in agri-food and rural systems. Keywords: Social media, Internet, Agriculture, Rural, Innovation, Communication, Canada, Ontario
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".