The Case for Using an Intergenerational Multi-Methods Approach in Community-Based Research
Bibliographic record
Abstract
Community-Based Participatory Action Research (CBPAR) is used in a variety of disciplines, including community development. However, intergenerational CBPAR research, particularly when using visual methods, has been uncommon in fields outside of those in the health domain. Given the success with which some health-related studies with vulnerable youth and adults from disadvantaged regions have applied this kind of research, we conducted a study using a similar approach on entrepreneurship and social and economic capacity building in a rural and remote region. Our CBPAR intergenerational multi-methods research project involved youth, adults, seniors, Elders (Indigenous spiritual leaders), and academic researchers as investigative co-leaders seeking findings useful for changing inequitable systems and practices. With these research partners, we employed a carefully selected set of qualitative data collection methods, including a variety of visual methods, designed to produce robust and actionable findings and knowledge mobilization opportunities. Our research design provided a powerful way to triangulate data while engaging with the broader community to co-produce knowledge across generations. One way we did this was through Indigenous language videos, featuring community members of all ages describing their perspectives on social and economic development in their communities. In this article, we describe how and why our intergenerational multi-methods approach helped us verify our data and enabled our partner communities to leverage the findings to enhance local wellbeing. In doing so, we develop the case for using intergenerational multi-methods approaches with visual method elements in business and other disciplines in which these methods are not often used.
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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 | 0.376 | 0.252 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.023 | 0.061 |
| Scholarly communication | 0.031 | 0.047 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.014 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 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".