Does evidence of network effects on firm performance in pooled cross‐section support prescriptions for network strategy?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Strategic prescriptions drawn from pooled cross‐sectional evidence of firm performance effects are not necessarily warranted. This is because firm characteristics can influence both the mean and variance of firm performance. Strategic inferences are warranted if empirically observed effects reflect increases in mean firm performance. If they reflect increases in firm performance variance, however, such inferences are warranted only if the increased odds of achieving high performance compensate sufficiently for the concomitantly increased risk of realizing poor performance. Our simulation study, which contrasts firm performance effects in pooled cross‐section and within‐firm over time, counsels caution when basing strategic prescriptions on pooled cross‐sectional studies of firm performance in general, and in the case of network effects in particular . Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it