Bibliographic record
Abstract
he purpose of this volume is to recount and celebrate three different journeys.First are the journeys of the authors, who have navigated the challenging and ultimately rewarding waters of community-based research (cbr).All of the authors in this volume are or have been affiliated with the Saskatchewan Population Health and Evaluation Research Unit (spheru) and Community-University Institute for Social Research (cuisr).With this volume we also celebrate the journeys over the past decade of our two research institutes to fulfill the founders' vision of genuine community-university research partnerships in the service of better lives for vulnerable people and groups.And third is our own journey as editors of this collection, seeking to build our partnerships and practice of community-based research (cbr) in the process of working with the authors.We have organized the volume to highlight examples of the successes and challenges of cbr across a range of projects in three areas of interest: ethical issues in cbr, issues that arise in cbr projects with an advocacy focus, and the impact of cbr projects.The first section, "Ethics of Community-Based Research, " presents chapters that address ethical issues faced in the development of university-community partnerships and the xviii Jeffery and Clarke engagement of communities, such as power imbalances, understanding and respecting cultural diversity, using culturally competent practices, participation, and community capacity building.The chapters addressing "Advocacy and Community-Based Research" discuss specific advocacy strategies or methods that have been employed, such as community meetings, community advisory groups, policy roundtables, and community-university partnerships.And, finally, case studies that highlight the "Impact of Community-Based Research" include examination of a specific cbr initiative that has led to an identifiable change in policy, program, or capacity development in reducing various inequalities.That spheru and cuisr should come together to produce this book is not surprising given our parallel and often overlapping journeys.As described by Randall and Waygood and Labonte in their forewords to this volume, some of the same people were engaged in discussions about community-university partnerships to advance health and quality of life in 1999.All were deeply influenced by the specific location, Saskatchewan, and its socio-economic, geographic, and cultural contexts.All were committed to adopting multiple perspectives and cbr principles, but each organization took a slightly different path to reach its goals.We will provide brief overviews of spheru and cuisr and then map the terrain of cbr that we share. SPHERUspheru was established jointly, in 1999, by the Universities of Saskatchewan and Regina as an interdisciplinary research unit committed to the promotion of health equity by understanding and addressing population health disparities through policy-relevant research.Researchers at spheru come from a variety of academic backgrounds, including geography, political science, anthropology, epidemiology, social work, economics, nursing, nutrition, and history.Although there is no unifying theory of population health per se (Coburn et al., 2003;Kindig & Stoddart, 2003;Labonte et al., 2002), these researchers draw on discipline-specific theories of health determining conditions related to social class, gender, culture, society, place, and time.The researchers actively engage with communities and policy-makers to accomplish the goals of the unit, which include building on the existing expertise, knowledge, and capacity of all research partners and exchanging research knowledge with communities and policy-makers through ongoing
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.072 |
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".