Improving Program Results Through the Use of Predictive Operational Performance Indicators
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
In Canada, in-depth evaluations of federal programs are intended to occur every 5 years. As such, evaluation is a periodic retrospective (lag) indicator examining results achieved versus program objectives. In a Canadian context, stand-alone evaluations have proved challenging to implement, time consuming, and not well adapted to annual management accountability needs. Consequently, there are important benefits from developing parallel ongoing operational performance measurements, complementing periodic evaluations as an integrated system. With links to program evaluations, ongoing performance feedback can include predictive (lead) indicators of progress, through operational linkages to a program’s intended long-term outcomes. The present case study examines program efficiency concerns demonstrating lead indicators as an “early warning system”—targeting problem areas, producing speedier program adjustments (including accountability and efficiency improvements) and also demonstrating potential to increase quality, timeliness, and usefulness of longer term in-depth evaluations.
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.
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 | 0.008 | 0.004 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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