Connecting Knowledge to People: Plain Language Summaries from the North Atlantic Forum
Bibliographic record
Abstract
The 2024 North Atlantic Forum was a collegial assembly to further the discussion surrounding public policy, regional development, place-based development and expanding the importance of identity and cultural development. Researchers, students, community development practitioners, and government representatives from across the North Atlantic region gathered to present findings, participate in panels and field trips. To amplify knowledge mobilization presented at the conference a series of plain language summaries were created to advance the discussion in various topics surrounding rural development and rural businesses. This initiative created a series of plain language research summaries on innovative approaches in building and maintaining sustainable rural livelihoods in a post-pandemic environment. This initiative mobilizes knowledge shared at the 2024 North Atlantic Forum to rural stakeholders in Ontario, ensuring this critical information reaches rural communities and businesses. The series of research summaries focus on topics of solar energy, food security, heritage tourism, housing, entrepreneurialism, and economic development. The purpose of the summaries is to amplify knowledge sharing, particularly among individuals/organizations that may not have been able to attend the conference. This initiative disseminated new knowledge outputs to key rural Ontario stakeholders, such as local governments, economic development actors, nonprofit organizations, and businesses. The research summaries generate an alternative tool in facilitating an accessible transfer of key messages in research while providing a pathway to practice and participation for rural community members at-large.
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 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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.015 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".