Clarifying the process of land-based research, and the role of researcher(s) and participants
Bibliographic record
Abstract
Despite significant research in environmental sociology, environmental sustainability, and cultural geography, the following questions remain ambiguous for many Indigenous communities: What constitutes land-based research and what is its purpose? How are researcher and participants situated in land-based research? Who has the power to select the research topic, research objectives, and research site? Who has the power to determine research protocols, data analysis and dissemination procedures? What can be learned from land-based research? Focusing on a relational participatory action research (PAR) project with the Laitu Khyeng Indigenous community in the Chittagong Hill Tracts (CHT), Bangladesh, this paper addresses the above questions as a means of advocating for land-based research. My learning journey in land-based research is a relational ceremony that not only reinforces my desire to create a bridge between researcher and participant needs but also serves as inspiration in rethinking the meaning of research from the participants’ perspectives.
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 | 0.287 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.057 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".