Bibliographic record
Abstract
This chapter concludes the book by summarizing all the chapters. In the lead chapter, findings of Hewett, Krasnikov and Hepworth support the notion that less developed institutional environments in emerging markets can negatively impact the overall intensity of international expansion by firms in those markets. In particular, high levels of corruption and less developed legal and judicial systems can reduce firms’ abilities and/or willingness to embark on an intensive strategy for global expansion. However, their findings demonstrate mixed results in terms of how less developed institutional environments impact emerging markets firms’ scope of expansion. The authors find that firms from more corrupt home markets submit applications to register their trademarks in fewer countries than firms from less corrupt home markets. However, stronger and more impartial legal systems also appear to impede the overall scope of firms’ geographic expansion. The authors also find that when markets are high in corruption and the strength and impartiality of the legal systems are low, the overall level of trademark applications from those countries is lower…
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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; both teacher heads agree on what is shown here.
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".