New approaches in cross-cultural management research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The field of cross-cultural management is expanding rapidly. Traditional approaches are being critiqued and new approaches put forward. The latter mainly adopts an interactionist perspective, pay more attention to context and different levels of analysis (local, regional, national etc.) and propose more qualitative methods as well as a more dynamic definition of culture. Our research is in keeping with this new shift and contributes to this renewal in two ways. First, it shows the variability of the perceptions of individuals from a given culture regarding the management practices existing in another culture when they find themselves working in that other culture. This variability is based on contextual elements that we have identified: duration of work experience in the country of origin, occupation of the respondent, quality of the relations with locals and so on. Then, the research reveals the link that exists between the quality of the respondents’ integration into this culture and their interpretation of the others’ management practices. These findings were obtained by combining a qualitative approach (some 40 semi-directed interviews) and a quantitative approach (a questionnaire administered to a population of more than 1000 respondents) among a population of French nationals working in Quebec and Quebecers working in France.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it