Management Research on Multinational Corporations: A Methodological Critique
Bibliographic record
Abstract
In the context of burgeoning research on multinational corporations (MNCs), this paper\naddresses the issue of the representativeness of databases of MNCs in Ireland. It identifies some important deficiencies in existing databases much used by scholars in the field. Drawing on the international literature, it finds that this problem also characterises research on MNCs in many other countries. In the Irish context, we find that the extant empirical research has generally excluded two key categories of MNCs, namely, (a) foreign MNCs which are not grant-aided by the main industrial promotions agencies and (b) Irish-owned MNCs. The paper outlines our experience in identifying and addressing these deficiencies and describes the methods that might be employed in more precisely defining the MNC population in Ireland. More generally the paper reviews some of the issues and obstacles confronting scholars investigating the MNC sector in Ireland and abroad.
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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 | 0.126 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.023 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".