Rarely pure and never simple: Assessing cumulative evidence in strategic management
Bibliographic record
Abstract
As empirical evidence in strategy has accumulated, scholars have shown increasing interest in assessing the empirical record of leading theories. Two methods of assessment have figured prominently: vote counting and meta-analysis. Recently, critics have denounced the former in favor of the latter. While meta-analysis is certainly a powerful assessment tool, we argue that both vote counting and meta-analysis are characterized by certain strengths and weaknesses and that these methods should be seen as complementary means of understanding bodies of empirical evidence. We provide guidance regarding when to employ each method and how to improve the process of cumulative assessment.
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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 | 0.530 | 0.878 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.045 | 0.037 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".