Bibliographic record
Abstract
Abstract Much of the work on industrial location, internationalization and innovation is based on firm- or firm-network-level research, but does not consider the role of industry-based professional communities that can be crucial in providing access to knowledge, resources and personal networks. These communities, whose membership reaches well beyond firms themselves, are indispensable components of firms’ everyday activities, yet are often overlooked when investigating firm behavior. This paper focuses on the one hand on the role of local communities and those individuals that form them, and on the other hand on how they link with international communities and become crucial facilitators of internationalization processes. In a co-evolutionary perspective, we investigate the role of local professional communities and the local-global interfaces that are created in internationalization processes, and how such localized activity may be associated with regional development. In a conceptual discussion, we propose that local professional communities and their local-international community connections are crucial to the capacity to engage in internationalization projects. From this, we discuss a number of related questions: First, who are the members of local professional communities and how do they create knowledge? Second, how do local professional communities develop and what are the driving forces that underlie their growth? Third, what are the conditions for the reproduction of local professional communities? We conclude by highlighting that the interrelationship between local and international communities is a critical feature of a permissive environment that facilitates corporate success in the internationalization process, and this favorable interaction between firms and their environment equally impacts the development prospects of the city-regions where they are located.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".