Icelandic National Culture compared to National Cultures of 25 OECD member states using VSM94
Bibliographic record
Abstract
Researchers such as Hofstede (2002) and House, Hanges, Javidan, Dorfman and Gupta, (2004) have defined well-known cultural clusters such as, Anglo, Germanic, and Nordic cultural clusters. However, Iceland was not incorporated in these studies and therefore the research question of this paper is: In relation to Hofstede´s five cultural dimensions where does Iceland differ in relation to 25 of the OECD member states using VSM94? A questionnaire was sent to students at the University of Iceland, School of Social Sciences by e-mail in October 2013. The five dimensions of national culture were measured using scales developed by Hofstede called VSM 94. The results indicated that Iceland differs considerably from nations such as Slovakia, Japan, India, Thailand and China, which were found high in PDI and the MAS dimension while Iceland was found to be high in IDV and low in PDI. When considering the 25 OECD countries, Iceland is more similar to the Anglo cluster, C3, Canada, New Zealand, United Kingdon, Australia and United States than the Nordic cluster, C1 i.e. Denmark, Sweden and Norway. Iceland is similar to those countries in relation to high IDV, low PDI but differs in the dimensions MAS and UAI where Iceland scores higher.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".