Network Evaluation in Practice: Approaches and Applications
Bibliographic record
Abstract
As more funders support networks as a mechanism for social change, new and practical knowledge is emerging about how to build and support effective networks. Based on extensive review of different types of networks and their evaluations, and on interviews with funders, network practitioners, and evaluation experts, the authors have developed an accessible framework for evaluating networks. This article describes the evaluation framework and its three pillars of network assessment: network connectivity, network health, and network results. Also presented are case examples of foundationfunded network evaluations focused on each pillar, which include practical information on evaluation designs, methods, and results, as well as a final discussion of areas for further attention.
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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 | 0.291 | 0.398 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.020 | 0.028 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".