Some Problems in Using Subjective Measures of Effectiveness to Evaluate Entrepreneurial Assistance Programs
Bibliographic record
Abstract
Two samples of entrepreneurs and small business owners who received assistance from entrepreneurship training programs were used to Investigate the relationships between (1) subjective measures of client satisfaction, (2) perceptions of performance Improvements attributable to the programs, and (3) objective measures of post-assistance business performance. The results show that subjective measures are not correlated with either attributions of performance or actual performance. Clients’ attributions of the portion of performance improvements attributed to the programs are generally correlated with the objective measurements. An implication seems to be that program evaluations relying exclusively upon participants’ satisfaction or subjective judgments of program effectiveness may lead to erroneous conclusions about a program's impact on venture performance. Conversely, measures of attribution, used In conjunction with objective measures, may be useful to support claims for causal connections between assistance programs and subsequent client performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".