Centering relationships to place for more meaningful research and engagement
Bibliographic record
Abstract
Research has the potential to simultaneously generate new knowledge and contribute meaningful social-ecological benefits; however, research processes and outcomes can also perpetuate extractive patterns that have manifested the climate, biodiversity, and social justice crises. One approach to enhance the societal value of research processes is to strengthen relationships with places of study and the peoples of those places. Deepening relational engagement with the social-ecological context and history of a place can lead to more accurate results and improved public trust in the scientific process and is particularly important for natural scientists who work at the interface of nature and society. We provide three actionable pathways that range from individual to systemic change to enhance place-based relationships within research systems: 1) deepen reflection and communication about relationships with places and peoples; 2) strengthen collaboration among research teams and partners; and 3) transform systems of knowledge creation to foster place-based roots. Action on any of these proposed pathways, but especially action taken across all three, can build empathy and connections to place and people, strengthening the meaningful impact of research both locally and globally.
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 | 0.049 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.042 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.004 | 0.057 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".