When Government Gets It Right: How a Strategic Visioning Process Aligned Nested Government Systems to Champion Local Relevance and Determination
Bibliographic record
Abstract
This theme issue of the Interdisciplinary Journal of Partnership Studies addresses government/civic partnerships. Do government services always orient toward hierarchies of domination? Our answer is a resounding no. This article offers as evidence the actions of one government funder that removed hierarchical barriers, working in partnership with diverse grantees to envision a program that prioritizes community relevance and participation. Even as our article revolves around a strategic visioning event, it is a culmination of a government funder living out its guiding principles of mutual respect, joint problem solving, and valuing diversity, as well as the values, experiences, and collaborative spirit that diverse grantees brought. Our collective stories offer a clear example of how a partnership-based government program can engage and promote the strengths, needs, and priorities of the community not only because it is the appropriate and respectful approach, but also because it leads to stronger program results.
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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 | 0.036 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.034 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".