Bibliographic record
Abstract
Building an integrated global strategy for ongoing growth and renewal across markets which are geographically remote and have differing native languages and cultures is undoubtedly harder than classical scholars of business strategy have cared to understand or admit. While our cumulative knowledge of the role of organizational learning in internationalization is considerable, it has generally been seen as knowledge transmitted from the home-organizational base to subsidiaries abroad. As such, current theories take little account of the capacity of the organizations to reverse the one-way vector of learning emanating from headquarters in order to learn endogenously, as it were, from knowledge resources available at the periphery and throughout its global reach. A major reason why theory has not advanced further in documenting and thus realizing the mechanisms by which firms can learn from their global footprint is that the methodologies used have been ineffective as they tend to take a "bird's eye" view of phenomena when what is needed is an 'up-close' and contextually-grounded approach. In this keynote speech, Professor Mary-Yoko Brannen will discuss new avenues for research methods that facilitate the understanding of complex, micro level contextually embedded phenomena where research settings are rife with multilevel cultural interactions. Integrating current research on the multifaceted nature of language used in global organizations and the boundary-spanning skillsets that bi- and multi-cultural individuals bring to today's workforce, Professor Brannen will discuss new methods for researchers as well as practitioners to positively influence global learning and innovation outcomes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".