Exercising Strategic Flexibility by <scp>MNEs</scp> During the 2008–2009 Global Financial Crisis
Bibliographic record
Abstract
ABSTRACT Given the merits associated with foreign direct investment (FDI), governments deploy policies to attract foreign multinational enterprises (MNEs). At the same time, governments deploy policies to smooth business cycles because of the costs associated with fluctuations in economic activity. The cross‐border presence of MNEs bestows upon them the strategic flexibility to adjust their activities across borders in response to recessions or financial crisis (or both). This raises the important question of whether, within any given domestic‐market context, the operations of both domestic and foreign MNEs magnify or mitigate business cycles. The current paper is focused on the 2008–2009 global financial crisis and the recession that followed, and the extent to which domestic and foreign MNEs operating in the Canadian market magnified its effects on the Canadian economy. This research is important because to the extent that MNEs magnify the effects of such financial crises, these additional costs must be offset against the benefits that come from the presence of MNEs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".