Exploratory scoping of place-based opportunities for convergence research
Bibliographic record
Abstract
Harnessing scientific research to address pressing societal needs requires careful alignment of resources, expertise, and research questions with real-world needs, timelines, and partnerships. Literature on best practices for place-based transdisciplinary research is underdeveloped on the question of choosing locations to help achieve this alignment. In practice, locations are often chosen based on convenience or prior experience—a strategy sometimes called opportunism. Here we explore a deliberative and exploratory approach to locations in contrast to this default opportunism. We introduce a general framework for the scoping of locations for research and engagement, and we apply the framework within a large (5-year, \$20-million, 13-institution) research project addressing coastal climate risks in the Northeast US. The framework asks project personnel to negotiate explicit project goals, identify corresponding evaluation criteria, and assess opportunities against criteria within an iterative cycle of listening to needs, assessing options, prioritizing actions, and refining goals. In the application, we elicit a broad range of objectives from project personnel. We find that a structured process offers opportunities to collaboratively operationalize notions of equity and justice that researchers increasingly invoke but seldom define. We find some objectives in tension—including equity objectives—indicating trade-offs that other projects may also need to navigate. We reflect on challenges encountered in the application, on near-term costs and benefits of the exploratory process, and on the characteristics of research efforts that may benefit from exploratory approaches to scoping locations for engaged research.
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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 | 0.276 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.024 | 0.018 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".