Towards a comprehensive framework for the evaluation of small and medium enterprise policy
Bibliographic record
Abstract
This research demonstrates the relevance of the evaluative cycle and its diverse methodological designs in small and medium enterprise (SME) policy. We structure our arguments based on the most common phases of the cycle, namely policy justification, needs, policy theory, implementation, impact and efficiency assessments. We use an in-depth case study of public assistance to an SME to illustrate how findings from these phases go beyond the results of the additionality practice in SME policy. We employ the findings as starting points to discuss several methodological designs for the evaluation of entire programmes, policies and systems.
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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 | 0.393 | 0.274 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.023 | 0.014 |
| Science and technology studies | 0.008 | 0.038 |
| Scholarly communication | 0.036 | 0.028 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".