Why are there (almost) no randomised controlled trial-based evaluations of business support programmes?
Bibliographic record
Abstract
Abstract Based on the achievements of randomised controlled trials (RCTs) in medicine, and the need for effective government interventions in support of business, some have advocated for the use of RCTs in the evaluation of business support programmes (BSPs). Notwithstanding these recommendations, the use of RCTs in the evaluation of BSPs has been resisted by (almost) all. Policy makers and managers are correct in their reluctance to undertake RCT-based evaluations for four reasons. First, while RCTs require the random allocation of support, judicious programmes select firms on the basis of potential and amenability to support. Second, while RCTs require treatments that exhibit low variability, the most effective BSPs draw upon substantive knowledge to provide support that is customised. Third, BSPs aim to produce outliers—firms whose performance is exceptional. When outliers are present, very large samples will be required to produce reliable results. Finally, an RCT may not yield a meaningful contribution to knowledge. The strength of an RCT is its ability to estimate the magnitude of the treatment effect under controlled conditions. But where much depends on the nature of participants and circumstances, we seek evidence of what works, for whom, in which circumstances, and why.
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 | 0.588 | 0.862 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".