Integrative Collaborative Activities: Public Deliberation with Stakeholder Processes
Bibliographic record
Abstract
Collaborative governance is the process of public, private, and non-profit sectors jointly developing solutions to public problems. This process—convening people from different sectors to work together on a shared issue—can yield the best solutions to public problems. Research and experience show that solutions created in a collaborative governance process are “better informed, stronger in concept and content, and more likely to be implemented,” according to Terry Amsler, Director of the Collaborative Governance Initiative. These solutions go beyond what any one sector could achieve on its own. They are more lasting and effective than solutions from traditional approaches. They are more lasting than legislative solutions because they will not be undone in the next year or legislative session. They are more effective than solutions from traditional processes because they integrate resources from across agencies and sectors to address the problems. In addition, these solutions are more likely to be implemented because stakeholders (interested parties) are involved in the process from the start and have a role in the final decision. This promotes ownership by stakeholders and thereby helps the solution be put into action promptly and without litigation. This report explores how leaders can create even better solutions by combining collaborative governance activities—engaging the public in discussion and implementing their ideas through a representative group of stakeholders. This type of integrated collaborative process could first engage the public in a dialogue to hear their values and ideas about an issue or a project. Then, a stakeholder group could implement the ideas that were developed in the public forum.
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 | 0.062 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".