Bibliographic record
Abstract
Purpose The aims of the research behind the paper are to understand better the present situation of the problem of going from training to learning and to try to suggest methods and solutions to improve the situation. Design/methodology/approach The author uses her experience in teaching cross‐cultural management and interviews with top executives to find out how some typical cultural factors influence management practices and employee behavior. Findings The key findings of the paper are the following: cultural factors play a great role in how companies are managed and people in them handled: controlled and motivated. But it looks like there is another important group of factors that influence all these managerial elements: this is the economic and political situation of any country. In the case of Hungary, which is a cheap production site for global and multinational companies, managers manage, control and motivate differently than for example “at home”, in a highly developed country. Research limitations/implications Further research on a larger sample is needed to support the ideas mentioned in the article better. Practical implications One implication could be to write a professional textbook on the topic. A further one is to put together a proposal for the government in order to focus the attention on the importance of learning in all kinds of institutions and at all levels. One practical result of the findings is these are already being taught by the author thorugh the international management courses for foreign students studying at the Corvinus University in Budapest. Originality/value The paper presents a research approach of trying to find relationships between cultural factors and learning approaches and philosophies. It can be of value for those interested in cross‐cultural research, and also for companies interested in finding the best approaches to learning in a particular society.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".