The institutionalization of evaluation matters: Updating the <i>International Atlas of Evaluation</i> 10 years later
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This text provides a comparative cross-country analysis of evaluation culture and the institutionalization of evaluation. The countries included in this research are the 19 OECD countries examined by the authors of the International Atlas of Evaluation 10 years ago (Furubo et al., 2002). The analysis is based on the results of an expert survey of four to five evaluation experts from different backgrounds for each country, as well as additional information from the literature. Using the nine indicators from Furubo et al. (2002) with a focus on the institutional characteristics of reforms, trends in evaluation culture over the last decade have been identified.
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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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it