Bibliographic record
Abstract
Organizational Demography The population of KMFs at any point in time depends on past rates of KMF creation and destruction. It also depends on past rates at which KMFs have been converted into LMFs, and vice versa. Given the small population of LMFs, the latter effects are negligible relative to the stock of KMFs. The number of LMFs at any point in time also depends on past rates of creation and destruction as well as past rates at which KMFs have become LMFs, and vice versa. However, transitions between the KMF and LMF structures play a non-trivial role in determining how many LMFs there are at a point in time, because such flows are significant relative to the overall population of LMFs. Understanding these processes is therefore an important objective in explaining why LMFs remain rare. Section 10.2 begins with birth rates of new firms and considers why there might be a systematic bias favoring the KMF structure at this stage. Section 10.3 addresses worker buyouts of KMFs, and Sections 10.4 and 10.5 take up the opposite process, the conversion of LMFs into KMFs. I discuss the evidence concerning survival rates of LMFs and KMFs in Section 10.6. Section 10.7 takes a brief look at the effects of the business cycle on the LMF population, and Section 10.8 examines the tendency of LMFs to cluster in particular industries, geographical regions, and historical eras.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".