Bibliographic record
Abstract
Below, I have selected one substantive additional reading for each chapter in the book. I based my selection on the criterion that each reading should convey a truly important message (or series of messages) complementing the chapter's storyline. The readings provide understanding critical to managerial practice in the international business strategy sphere. I have tried to be eclectic in my selection. In some cases the additional reading is more academically grounded; in other cases it is more immediately practice oriented. But the reading's main purpose is always the same: allowing the student of international business strategy to learn beyond the present book, thereby further sharpening the mind about the topic she or he is passionate about. Chapter 1: Dunning, J. H. and Lundan, S. (2008). Multinational Enterprises and the Global Economy , 2nd edn, Cheltenham: Edward Elgar. Dunning and Lundan's volume complements the present book in two important ways. First, it provides an overview of the environment of multinational enterprises, with much attention devoted to the world wide picture of foreign direct investment and business–government interactions. Second, it addresses in great detail the wide variety of impacts MNEs have on society. Dunning and Lundan's volume is by far the most detailed account of multinational enterprise activity ever written. Chapter 2: Szulanski, G. (2003). Sticky Knowledge: Barriers to Knowing in the Firm , London: Sage Publications.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.821 | 0.514 |
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".