Bibliographic record
Abstract
When the grandfather of modern economics, Adam Smith, was preparing his Wealth of Nations almost a quarter of a millennium ago, the workforce of businesses would typically be counted in single or double figures. Now the world's biggest firms, such as Wal-Mart, have a payroll of well over a million, bigger than the population of some entire nations. This paper reviews some of the reasons for this growth, and considers whether it might continue forever. Powerful evidence exists of potential scale economies, in business functions from R&D to finance, and industries from pin production to pharmaceuticals. And these do indeed translate into remarkable performance records for some industrial giants. But surprisingly, on average, bigger firms do not enjoy above average profitability and, on the whole, giant firms are not gaining on the world economy. This paper reviews some of the market and managerial constraints on size, and considers innovative efforts – ranging from the New Zealand dairy industry to the McDonald's chain – to reconcile global-level scale economies in some functions with local autonomy in others. In passing, the paper notes a disturbing array of incentives tempting some managers to expand their empire, even when that is not in the shareholders' interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".