The Gulf of Maine Environmental Information Exchange: Participation, Observation, Conversation
Bibliographic record
Abstract
In this paper we describe an attempt to create an inclusive and participatory information sharing network across a large geographic region, the Gulf of Maine. This network aims to contribute to the health of the region's human and natural environments through facilitating partnerships among individuals and organizations that are already working toward this goal. Initiated at a time when cooperation, public learning, and information sharing increasingly depend on digital information technologies, this effort represents a turn away from earlier attempts to create centralized data sharing systems toward a more people-centered and project-centered approach. After introducing the Gulf of Maine Environmental Information Exchange and its region, particular projects will be described, along with the on-line technologies that are being applied including those related to digital mapping. A description of the purposes of the Information Exchange follows, with details about a network organization which is being shaped based on principles that have emerged through participant interactions. Public participation GIS and the community-based fisheries management movement are presented as examples of participatory governance that have contributed to discussions within the Information Exchange. We conclude with a summary of the accomplishments of this network building process and the challenges its participants recognize at this time.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".