An application of Hofstede's values survey module with aboriginal and non-aboriginal governments in Canada
Bibliographic record
Abstract
de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par telecommunication ou par I'lnternet, preter, distnbuer et vendre des theses partout dans le monde, a des fins commerciales ou autres, sur support microforme, papier, electronique et/ou autres formats XI List of Annexes Annex 1-1 VSM Indices for 40 Countries 220 Annex 1-2 VSM Indices for 50 Countries and 3 Regions 221 Annex 2 Bivariate Correlations Among VSM Indices for Four Dimensions 222 Annex 3 Seven Dimensions of Culture Summarized from Trompenaars 223 Annex 4 10 Motivational Types of the Schwartz Value Survey 225 Annex 5-1 Rokeach's Terminal Value Averages and Composite Ranks 227 Annex 5-2 Rokeach's Instrumental Value Averages and Composite Ranks 228 Annex 6-1 McCarrey etal's Figure 1 Concerning Terminal Values 229 Annex 6-2 McCarrey etal's Figure 2 Concerning Instrumental Values 230 Annex 7 Countries of the "Anglo Cluster" 231 Annex 8 Punnett and Withane's (1990) Hypotheses and Test Results 232 Annex 9 Cultural Differences Between Canada and the United States 233 Annex 10 Generic Survey of Government Employees A Test of the Values Survey Module in the Context of Aboriginal and Non-Aboriginal Governments in Canada 234 Annex 11 Research Description and Cover Letter for Aboriginal Governments 252 1 An Application of Hofstede's Values Survey Module with Aboriginal and Non-Aboriginal Governments in Canada ;1990))
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".