Unpacking Community Participation in Research: A Systematic Literature Review of Community-based and Participatory Research in Alaska
Bibliographic record
Abstract
Although concepts of “community” and “participation” have been heavily critiqued in the social sciences, they remain uncritically applied across disciplines, leading to problems that undermine both research and practice. Nevertheless, these approaches are advocated for, especially in Indigenous contexts. To assess the use of these concepts, we conducted a systematic literature review of community-based and participatory research in Alaska, USA, where social change has been rapid, having ramifications for social organization, and where participatory and community-based approaches are heavily advocated for by Alaska Native organizations. Conceptualizations of community and participation were extracted and analyzed quantitatively and qualitatively. The majority of articles showed a lack of critical consideration around both terms, although this was especially the case in reporting around community. While this lack of critical consideration could lead to issues of local elite co-opting research, an alternative interpretation is that Western sociological literature surrounding community is not transferable to Indigenous contexts.
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 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 | 0.174 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.041 | 0.035 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".