Bibliographic record
Abstract
Multinational corporations (MNCs) are complex, multifaceted entities. They can be studied through any number of conceptual lenses, and to some degree we, as researchers, are likely to see in them whatever we are looking for. MNCs have formal structures and control systems; they can be modeled as social networks; they are an arena for political and power games; they exhibit cultural disconnects. MNCs are also organic entities that evolve with the changing business environment, often with highly porous boundaries. The view of the MNC we are interested in here is an entrepreneurial one; that is, we are concerned with how individuals (within the MNC) actively pursue new opportunities without regard to the resources they control (Stevenson and Jarillo, 1990). Moreover, we are particularly interested in entrepreneurship that comes from managers of foreign subsidiaries that are relatively limited in their resources and/or their degrees of freedom. By focusing on these somewhat unusual individuals, and the initiatives they pursue, we are able to open up important broader issues regarding the way that MNCs evolve over time, and their sources of competitive advantage. This entrepreneurial perspective is one the first author mapped out almost 20 years ago in his doctoral thesis (Birkinshaw, 1995). This research was conducted in Canada in the aftermath of the Free Trade Agreement with the USA, and it documented issues and challenges that managers of foreign-owned subsidiary companies in Canada were preoccupied with.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".