Encouraging Knowledge Diffusion through the Peer Group Learning Model
Bibliographic record
Abstract
Producers learn from each other by sharing their experiences with implementing management practices – business, personnel, production - on their farms, while positively contributing to mental health and well-being (Betker and Lemoine, 2022; Farm Management Canada, 2012). The purpose of this project was to pilot peer learning groups to test their effectiveness as an alternative way to reach producers and encourage the involvement of early adopters in knowledge diffusion. Peer groups, comprised of Saskatchewan livestock producers, were formed to establish a peer-to-peer connection. Each peer group consisted of 5-8 operations, varying in their production model and location in the province. Meetings were organized throughout the year, being mainly virtual, with an option of one or two in-person meetings for each group. WhatsApp chats were created to encourage discussion outside of the meetings. Groups identified learning priorities for meetings based on mutual interests. Meetings were 90 minutes long and consisted of upcoming event notifications, guest speakers, and facilitated discussion. E-mails emphasizing key messages and relevant information were shared after every meeting. The peer group model is being expanded, with plans to create groups targeted to certain demographics, starting with young producers. An average of 60% of peer group members consistently attended their group’s meetings. Two-thirds of participants in the first iteration of peer groups had never previously participated in a peer group, and all were somewhat or very likely to recommend peer group participation to a friend. These groups amplified extension messaging and provided a sense of community among the members. Utilizing technology to form peer groups can increase knowledge diffusion by bringing early adopters from across the province together to share their experiences. This amplifies extension of practices and increases the speed of adoption on a larger scale.
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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 | 0.024 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".