Developing the next generation of community-based researchers: tips for undergraduate students
Bibliographic record
Abstract
Universities and funding agencies are increasingly calling for collaborative research between community partners and academics. When combined with faculty roles in training the next generation of researchers, these collaborative frameworks can present a challenge to undergraduate students seeking experience with research activities—both in terms of the types of needed training and the timelines involved. The quality and effectiveness of student research experiences, however, will have longstanding impacts on their future research careers, as well as repercussions pertaining to the community experience with the research process. The purpose of this study is to provide primarily undergraduate students with information about how to get the most out of their community-based research experiences. Given geography's traditional strengths as a field-engaged discipline, community-based research is a natural fit for geography and brings renewed vitality to the discipline. Key topics to be addressed include finding community research opportunities, identifying what you should know and what you should ask before engaging with a research team, how to obtain a breadth of research skills and experiences, researcher etiquette and demeanour in the community, budgeting, time management and developing long-term, meaningful relationships with communities.
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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 | 0.042 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".