Empirical Analysis II: The Number of Business Units and their Average Size over the Long Run: Models of Industrial Development
Bibliographic record
Abstract
4.1.1 The empirical evidence provided in chapter 3 shows that in the last quarter of the twentieth century industrialized countries witnessed a shift in manufacturing employment towards smaller business units. This took place — albeit to different extents — in almost all countries in relative terms, but it also coincided with shifts in absolute numbers in only two of the six countries included in the analysis — namely, Italy and Japan, that is the two ‘late comer’ industrial economies. This overall tendency represents a sharp reversal of the trend experienced by all countries since (at least) the end of the Second World War, which consisted of a constant growth of absolute employment levels in large firms (a development that is far less evident, as we have seen, as far as establishments are concerned). In (relatively) older industrial countries, then, changes in the shape of the business size distribution were basically driven by the downsizing of large firms, vis à vis a substantial stability in the number of employees in smaller ones. Such being the case, the observed shifts in overall employment mainly represent the outcome of the changing behaviour of larger units — that is, the reduction (at least in terms of the number of employees) in their average size.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".