Community-Based Research in the Classroom and Beyond \n
Bibliographic record
Abstract
Undergraduate students in geography, as with many other social science disciplines, are taught the principles of social and public policy, community resilience and development and other concepts and theories around creating a just and equitable society. However, rarely are these students provided with the opportunity to apply these principles to the very places they aspire to improve. Rarely do undergraduate students, who represent the emerging generation of new researchers, receive the training and support required to undertake research which is community-based in nature. Here, we define community-based research as those research initiatives which emerge from community and regional concerns and are taken up through partnerships between both community (governmental and/or non-governmental bodies) and university players. The community- based research that we focus on in this presentation is directly related to community and regional planning and development in rural Newfoundland and Labrador. \nWe see tremendous value in encouraging and promoting community-based projects at the undergraduate level by involving students in the research process. This is a transformative process because it requires students to critically engage with those principles they have acquired in the university setting and broadens their understanding of community dynamics ‘on-the-ground’. However, this type of knowledge exchange is by no means a one-way process. Through investigating two different “community-classroom” projects offered in a third-year geography class at Memorial University, we illustrate that the knowledge exchange that has taken place benefited not only the students, but has also assisted community players as well as the instructors, reaffirming the value of bringing community-based research into the classroom. \n
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".