A framework for entry: PAR values and engagement strategies in community research
Bibliographic record
Abstract
The purpose of this article is twofold: to explore the entry process in community-based research when researching sensitive topics; and to suggest a framework for entry that utilises the values of participatory action research (PAR). The article draws on a collaborative community-university research study that took place in the Waterloo and Toronto regions of Ontario, Canada, from 2005–2010. The article emphasises that community entry is not only about recruitment strategies for research participants or research access to community but it is also concerned with the ongoing engagement with communities during various stages of the research study. The indicator of success is a well established and trusted community-researcher relationship. This article first examines this broader understanding of entry, then looks at how community research entry can be shaped by an illustrative framework, or guide, that uses a combination of participatory action research (PAR) values and engagement strategies. Key words: research entry, community engagement, participatory action research, mental health and cultural diversity
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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 | 0.140 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.111 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".