LEARNING ORIENTED EVALUATION OF RECONSTRUCTION PROJECTS
Bibliographic record
Abstract
Scholars and practitioners agree that major improvements are required in the performance of reconstruction projects. However, how should one evaluate the performance of a reconstruction project? And, how can these evaluations be used to affect change in future projects? NGOs and funding bodies have widely adopted the logical framework approach (LFA) as a method for evaluating the performance of international development projects; however for learning-oriented evaluation, the LFA has major limitations, even if it has proven its utility for internal 'audit ' evaluations. (It allows the funding bodies and NGOs to establish the inputs, activities, outputs and results of a project, and to account for how money was spent and what was achieved). However, in learning-oriented evaluation, the goal is to develop a holistic understanding of the project's impacts, including both the expected and unexpected outcomes, for the purpose of gaining insight on how to improve the next project. As it exists now, the LFA is not particularly suited to this type of evaluation, but no other method is commonly practiced. This paper proposes a method for learning-oriented evaluation particularly adapted for reconstruction projects. It looks at how these evaluations are used by international development agencies, such as the Canadian International Development Agency (CIDA), to develop a body of knowledge that can be used by their partners to improve future project performance.
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 | 0.079 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| 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".