The Ecology of Organizational Growth: Chinese Law Firms in the Age of Globalization
Bibliographic record
Abstract
In the global legal services market, China has some of the youngest law firms but also some of the largest. In the early 21st century, several Chinese law firms have grown into mega law firms, with thousands of lawyers in a large number of domestic and overseas offices. This study uses the case of Chinese law firms to develop an ecological theory of organizational growth following the Chicago school of sociology. The authors argue that firms coexist and interact in an ecology consisting of other firms in the same industry. These firms occupy different ecological positions and generate various processes of interaction with one another. In their organizational growth, four species of Chinese law firms (global generalists, elite boutiques, local coalitions, and space rentals) have engaged in a variety of ecological processes, including competition, symbiosis, accommodation, assimilation, purification, and proletarianization. By locating firms in a social space and investigating the spatial and processual patterns of their growth, this ecological theory presents not only a system of social classification but also a logic of temporal change through social interaction.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".