Joining collective impact and community science: a framework for core collaborative community science
Bibliographic record
Abstract
We propose the core collaborative community science framework, an original conceptual framework that integrates and modifies best practices from community science and collective impact groups to support investigations of environmental health and justice. The core collaborative community science framework differs from more typical frameworks for community science, which often frame projects as static and either scientist or community led; these framings can limit the potential for co-production and action-oriented models of science. Frameworks are lacking to help community science collaborators determine the contributions and leadership needed to initiate, sustain, and link together multiple projects that jointly support local learning and action, as well as contribute to broader scientific knowledge of complex social-ecological systems. The core collaborative community science framework offers three main innovations and contributions: (1) It invests in a core collaborative group structure, designed to increase community capacity and resilience through an expanded network of partners dedicated to the reduction of systematic inequities and injustices; (2) It seeds and supports multiple, diverse research projects implemented across complex social-ecological systems, focusing first on community-identified needs, and then on the questions community science can help answer; and (3) It facilitates dynamic shared responsibilities and leadership for partners from community, research, and government institutions, recognizing the need for shared contributions at all project phases. We offer examples from the Green Duwamish Learning Landscape in Washington, USA to show how project partners have coordinated their work focused on social, ecological, and human health and navigated challenges related to funding, staffing, and governance. We share insights on how to help integrate community science within the social fabric of communities, especially those faced with environmental health and justice challenges.
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 | 0.053 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.012 | 0.054 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".