Against Excessive Rhetoric in Impact Assessment: Overstating the Case for Randomised Controlled Experiments
Bibliographic record
Abstract
The recent attention afforded to randomisation, or Randomised Control Trials (RCTs), in impact assessment is a welcome development. The case for RCTs in international development, however, has been quite overstated. This article critically examines the seminal model underlying RCTs, the Holland-Rubin Framework, with a view to make four claims about RCTs: (i) they have limitations as conceptions of causation; (ii) their ‘idealised’ model of causal inference is undermined by implementation issues; (iii) they are not necessary to make internally valid statements about impact; and (iv) in general, they do not provide sufficient information for many purposes of impact assessment. The key argument is that ultimately, the choice of approach to impact assessment should be driven by the research question at hand and not by the alleged superiority of method.
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 | 0.824 | 0.884 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.008 | 0.127 |
| Scholarly communication | 0.023 | 0.043 |
| Open science | 0.012 | 0.020 |
| Research integrity | 0.042 | 0.060 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".