The pedagogy of community research: moving out of the ivory tower and into community organisations in Canada
Bibliographic record
Abstract
To describe a model of teaching/preparing students to engage in community research To focus on the collaborative elements of the project To examine power issues associated with communities setting the research agenda To discuss the relationships that emerge between student researchers and community organisations as they collaborate on research Universities are increasingly engaged in community research (CR) as a means of attracting and retaining students (Gallini and Moely, 2003). The approach to preparing researchers discussed in this chapter has benefits for the university, students and community organisations. Students receive relevant training and experience when engaged in the community and engagement is seen as an indicator of academic success (Billig, 2009). In an era of downsizing and budget restrictions there is increased emphasis on fiscal accountability. Funders are demanding evidence of effectiveness as a prerequisite for continued resources. Community organisations can benefit from students’ expertise in evaluating their programmes and defining effectiveness in order to meet their funders’ demands. In this context universities and students can make a significant contribution to services provided to service users, but they must acquire the necessary skills to form collaborations that embrace the research agendas of the community. This chapter considers a number of key questions: how do you prepare students to collaborate with community organisations in research projects? How do the various research partners (faculty, students, community organisations, service providers, service users) experience this collaboration? What are the power issues that students face when engaging in research when the community sets the research agenda? We will address these questions based on several years of experience teaching about community research to social work graduate students while they engage in research with social service community partners in the Ottawa region in Canada.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.018 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".