Partnerships for Knowledge Building: An Emerging Model
Bibliographic record
Abstract
Knowledge Building is approached in this study from an organizational perspective, with a focus on the nature of school-university-government partnerships to support research-based educational innovation. The paper starts with an overview of what is known about effective partnerships and elaborates a conceptual framework for Knowledge Building partnerships based on a review of literature and two case studies of school-university-government partnerships. In one case, a Ministry of Education wanted to bring more vitality into schools of small remote villages, and in the other case another Ministry of Education wanted to renew its school-based international cooperation profile. Emerging from this work is a three-component model for going to scale with Knowledge Building partnerships: Knowledge Building as a shared vision; symmetric knowledge advancement; and multi-level, research-based innovation. Characteristics of, and conditions for, effective partnerships for Knowledge Building are elaborated, and an emerging model is developed to help communities establish effective partnerships and contribute to this evolving model.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.014 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".