Combining internal and external evaluations within a multilevel evaluation framework: Computational text analysis of lessons from the Asian Development Bank
Bibliographic record
Abstract
Although the literature on evaluation has theorized about the distinction between internal and external evaluation, hardly any research has compared them empirically. This article examines whether the lessons of internal evaluations differed from those of external evaluations in the case of international development aid. It analyzes internal evaluations of the Asian Development Bank for nearly 1000 sovereign interventions across 38 countries in the Asia-Pacific during 1996–2016, using computational text analysis or text mining techniques. The results show that internal evaluations focused more on micro- and meso-level characteristics, while external evaluations laid more emphasis on meso- and macro-level constructs, such as dimensions of policy and the institutional environment in the recipient country, or its level and rate of economic growth. The article concludes that internal and external evaluations can be combined to create a multilevel evaluation framework that integrates micro-, meso-, and macro-level lessons to facilitate better learning.
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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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".