Understanding intercultural dynamics
Bibliographic record
Abstract
Purpose The purpose of this paper is to provide an overview on the state of the field in intercultural dynamics on competition and cooperation at the individual, team, and organizational levels. The authors integrate previous studies from multiple disciplines to articulate the contextual importance of intercultural dynamics. The authors also suggest three overarching themes to expand the field of research on intercultural dynamics. Design/methodology/approach The authors use an integrative literature review to articulate the importance of intercultural dynamics, provide an introduction to the new contributions in this special issue, and propose new directions for future research. Findings Intercultural dynamics research has the potential to expand in three overarching areas: constructive controversy, collaborative communication, and global competency and identity at multiple levels. Research limitations/implications Intercultural dynamics is still a nascent field emerging from cross-cultural and strategic management. The authors hope the review lays the groundwork for more studies on intercultural dynamics at the interpersonal, team, organizational, and mixed levels of analysis in both theory building and empirical works. Practical implications Understanding intercultural dynamics in competition and cooperation can help individuals and managers in multinationals and born global organizations navigate cultural complexity and foster cooperation. Social implications The authors hope the ideas on intercultural dynamics can facilitate collaboration and reduce conflict in intercultural encounters at the individual, organization, and societal levels. Originality/value This paper offers an overview on the state of the field and lays groundwork for more systematic inquiries on intercultural dynamics in competition and cooperation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".