Bibliographic record
Abstract
Abstract: This article examines how an evaluation unit in a federal government department transformed itself from a traditional function focusing on a few time-consuming and resource-intensive evaluation studies per year to a more service-oriented unit that has added results measurement to its core functions. It went down this road to increase the impact it was having on individuals, programs, and the department as whole. Along the way it discovered a new breed of evaluator: outgoing people interested in teaching, coaching, and facilitating. It found that there is more results measurement work out there than one unit can handle, and it learned to go where impact is highest. It had to cope with good evaluators being lured away by programs, and it recognized threats to evaluation independence. In the end, it has become a champion of the evaluator as results measurement specialist and is reaping the benefits of better results data, more astute evaluators, a more dynamic and stimulating work environment, and a larger and more visible impact.
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
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 | 0.021 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.048 | 0.013 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".