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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.092 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.025 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".