Bibliographic record
Abstract
This issue of CJPE reflects the importance of context, community, voice and relationships in evaluation practice, whether in an Indigenous urban community in the US, a virtual environment during the pandemic, a provincial office in PEI, or in communities in Southeastern Africa.Our need to build understanding and connection grounds our practice.I am particularly delighted to introduce this issue of CJPE, as it provides our first Indigenous publications in the new section of the journal "Roots and Relations: Celebrating Good Medicine in Indigenous Evaluation," co-edited by Larry Bremner and Nicole Bowman.Tere are three submissions in our new Roots and Relations section: a submission by Sofa Locklear, Martell Hesketh, Natalyn Begay, Jennifer Brixey, Abigail Echo-Hawk, and Rosalina James describes the use of an urban Indigenous framework designed to empower the community to reclaim their narratives and tell their stories.Te paper by Melanie Nadeau, Vanessa Tibbitts, Ryan Eagle, and Gretchen Dobervich describes the use of an Indigenous evaluation framework to evaluate a state-wide health improvement plan.Te third submission is a poem and narrative piece co-written by Katie Boone and Sharon Attipoe-Dorcoo.T ese three submissions inspire us to refect on evaluation as a way to build connections to people and place, build capacity, and empower people to share their stories and create their own narratives.Tere are also four other papers in the issue.Te article by Muazzez Nihal Öykü Ülker, Esa Kerimoğlu and Şaban Berk discusses the use of metaevaluation as a resource for assessing evaluation quality by helping to inform evaluators of the utility, feasibility, propriety, and accuracy of their work.Te next article is a practice note by Bobby Tomas Cameron that describes an evaluation policy development process that, through the co-development of codifed standards and guidelines, led to new connections among coworkers and helped build evaluation capacity within the organization.Te practice note written by Caitlin Blaser Mapitsa describes a baseline study that used a participant coded narrative process to understand how people in the Limpopo and Okavango River Basins understand resilience across the diversity of their contexts.Te third and f nal practice note by Paisley Worthington, Cheryl Mak, Michael Holden, and Michelle Searle explores the use of the Collaborative Approaches to Evaluation (CAE) principles to explore the nature of connection and relationship building in a virtual environment.We also have three book reviews in this issue: Van den Berg, Hawking and Stame (
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.009 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.154 | 0.101 |
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, unvalidatedLabeled directly by 3 models reading the full record.
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".