A Journey Through Five Evaluation Projects with the Same Analysis Framework
Bibliographic record
Abstract
Abstract: Evaluation is increasingly called upon to act in support of the management and accountability process, regardless of the field of activity. Often conducted in a complex and changing environment, it must make allowances for different interests and challenges that might occur at a number of levels. In this context, the analytical framework developed by the author may prove to be an interesting tool to organize planning of the evaluation process and for the interpretation of related results. In addition to a description of the five dimensions of this framework, a number of examples of use will be presented. A critical analysis of the strengths and weaknesses of the analysis framework, along with its principal contributions to the evaluation processes where it is in use, will be conducted based on comments issued to date by some of its users.
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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 | 0.145 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".