Developmental evaluation exemplars : principles in practice
Bibliographic record
Abstract
1. State of the Art and Practice of Developmental Evaluation: Answers to Common and Recurring Questions, Michael Quinn Patton 2. Cultural Responsiveness through Developmental Evaluation: Indigenous Innovations in Sport and Traditional Maori Recreation, Nan Wehipeihana, Kate McKegg, Veronica Thompson, & Kataraina Pipi 3. Using Developmental Evaluation to Support College Access and Success: Challenge Scholars, Srik Gopal, Katelyn Mack, & Cris Kutzli 4. Nine Guiding Principles to Help Youth Overcome Homelessness: A Principles-Focused Developmental Evaluation, Nora F. Murphy 5. Fostering Learning through Developmental Evaluation with a Nontraditional Arts Organization and a Traditional Community Funder, Jamie Gamble, Shawn Van Sluys, & Lisa Watson 6. Science and How We Care for Needy Young Children: The Frontiers of Innovation Initiative, Julie Asher, Nathaniel Foote, James Radner, & Tassy Warren 7. Developmental Evaluation's Role in Supporting Community-Led Solutions for Maori and Pacific Young People's Educational Success: The ASB Community Trust Maori and Pacific Education Initiative, Kate McKegg, Nan Wehipeihana, Moi Becroft, & Jennifer Gill 8. Developmental Evaluation in the McKnight Foundation's Collaborative Crop Research Program: A Journey of Discovery, Marah Moore & Jane Maland Cady 9. An Example of Patch Evaluation: Vibrant Communities Canada, Mark Cabaj, Eric Leviten-Reid, Dana Vocisano, & Mabel Jean Rawlins 10. Outcome Harvesting: A Developmental Evaluation Inquiry Framework Supporting the Development of an International Social Change Network, Ricardo Wilson-Grau, Paul Kosterink, & Goele Scheers 11. Adapted Developmental Evaluation with USAID's People-to-People Reconciliation Fund Program, Susan M. Allen, David Hunsicker, Mathias Kjaer, Rebekah Krimmel, Gabrielle Plotkin, & Kelly Skeith 12. Creating Safety to Explore: Strengthening Innovation in an Australian Indigenous Primary Health Care Setting through Developmental Evaluation, Samantha Togni, Deborah Askew, Lynne Rogers, Nichola Potter, Sonya Egert, Noel Hayman, Alan Cass, & Alex Brown 13. Leadership's Role in Building the Education Sector's Capacity to Use Evaluative Thinking: The Example of the Ontario Ministry of Education, Keiko Kuji-Shikatani, Mary Jean Gallagher, Richard Franz, & Megan Boerner 14. Developmental Evaluation in Synthesis: Practitioners' Perspectives, Kate McKegg & Nan Wehipeihana 15. The Developmental Evaluation Mindset: Eight Guiding Principles, Michael Quinn Patton
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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 | 0.213 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.010 | 0.037 |
| Scholarly communication | 0.033 | 0.022 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".