Knowledge Profiling as Emergent Theory in Community-Based Participatory Research
Bibliographic record
Abstract
BACKGROUND: Many sources of valid knowledge may be relevant to a research question. Communities need a mechanism to explore the full range of knowledge that could enrich community-based research. A knowledge profile (KP) is an integrated description of the knowledge and expertise that, once assembled, can help to explore a research issue. OBJECTIVE: This article describes the establishment of a KP as a purposive process whereby the initial research team identifies the kinds of knowledge that can help to articulate and refine a research question, and assemble the right research team and resources. METHOD: The KP process is conducted by a core team, which may expand to include additional expertise. The four phases of a KP are (1) creating the research space, (2) articulating and negotiating, (3) identifying the research question, and (4) creating the resource inventory. The process is illustrated by a case study. The outcomes of a successful KP include an inventory of existing and required resources, a strong research team operating in an ethical and safe research space, and clear articulation of the research question. The KP can be revisited regularly throughout a project to evaluate the effectiveness of the research team. CONCLUSION: KP provides a road map for community-based research teams to navigate through the early phase of research development.
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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 | 0.222 | 0.201 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.014 | 0.078 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.006 | 0.005 |
| 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".