Exploratory Scoping of Place‐Based Opportunities for Convergence Research
Bibliographic record
Abstract
Abstract Harnessing scientific research to address societal challenges requires careful alignment of expertise, resources, and research questions with real‐world needs, timelines, and constraints. In the case of place‐based research, studies can avoid misalignment when grounded in the realities of specific locations and conducted in collaboration with knowledgeable local partners. But literature on best practices for such research is underdeveloped on how to identify appropriate locations and partners. In practice, these research‐design choices are sometimes made based on convenience or prior experience—a strategy labeled opportunism. Here we examine a deliberative and exploratory approach in contrast to default opportunism. We introduce a general framework for scoping place‐based opportunities for research and engagement. We apply the framework to identify climate‐adaptation planning decisions, rooted in specific communities, around which to organize research and engagement in a large 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. 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 and on near‐term costs and benefits of the exploratory process.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".