Measuring Academic Capacity: Research in Relationship
Bibliographic record
Abstract
tective factor, contributing to that community’s physical, mental, spiritual, and emotional health. Academic researchers working with six First Nations and one Metis Settlement asked, “What is our role as academic partners in building capacity for communitybased participatory research?” The goal was to understand changes in the researchers’ capacities and their roles in building the capacity of community members. The Public Health Agency of Canada’s Community Capacity Building Tool (Public Health Agency of Canada, 2007) served as the framework for two focus groups. A thematic analysis of the focus group transcripts resulted in insights into researcher capacity and potential contributions to community capacity building. Focus group participants validated the interpretations and four themes that emerged from the data. Theme 1. Language and measures. The language and tools for measuring capacity, as described in existing literature, define and explore capacity from a Western worldview. In consultation with community, the authors learned that measures of capacity building based on an Indigenous worldview can include cultural identity, life purpose, community engagement, transmission of traditional knowledge from elders to youth, and participation in cultural cer emonies. In response to time-sensitive pressures to measure and document capacity, researchers often overlook the importance of co-creating relevant and meaningful measures. It is in the act of co-creation, where worldviews overlap, that researchers and com munity members contribute to each other’s capacity for research, sustainability, and, ultimately, community health.
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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 | 0.033 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".