Approach Paper: Evaluability Review of Bank Projects 2012
Bibliographic record
Abstract
In 2011 OVE began using the DEM and the self-evaluation system as a way to assess the evaluability of the Bank's portfolio by validating the DEMs of a sample of projects. The first exercise was used as pilot to set the standards OVE would use and to inform the Board and Management of the new approach. Since the DEM's criteria and process had just been revised in early 2011, OVE also used this pilot exercise to assess the DEM as an evaluability tool. The main findings of the first validation exercise were that OVE's evaluability scores were similar to Management's scores for sovereign-guaranteed (SG) operations, while OVE was unable to validate the scores of non-sovereign-guaranteed (NSG) projects. OVE recommended a full revision of the NSG DEM, and some refinements in the SG DEM. Management has worked on OVE's recommendations; as a result, a few changes will be implemented in the SG DEM in January 2013, and a new DEM is being developed for NSG projects. Since the NSG DEM is under development, OVE will wait until the new instrument is implemented to include NSG projects in the evaluability review exercises and be able to describe the evaluability of the Bank's overall portfolio. The new tool is expected to be launched in the second quarter of 2013.
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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 | 0.096 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".