Bibliographic record
Abstract
The obsolescing bargain (OB) model analyzes bargaining between a host country (HC) government and a multinational enterprise (MNE) at time of entry and the circumstances under which the original bargain does or does not erode over time. The model has traditionally focused on the dyadic relationship between the MNE and nation state. However, if a second wave of foreign multinationals should enter the HC, the relationship is no longer dyadic but trilateral: the host government, the first mover firms and the latecomers. What happens to the original and to subsequent MNE-state bargains? We incorporate recent insights on the liability of foreignness, transaction cost economics, multimarket competition and the resource-based view (RBV) into a theoretical model of sequential entry by rival multinationals. We find that liability of foreignness, firm rivalry and governance inseparability are key factors determining winners and losers in the sequential bargains. International institutions and home country governments are external forces that can also affect bargaining outcomes. We test our model's propositions on a longitudinal case study of public policy decisions in the Canadian auto industry.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".