The Armed Forces of Australia, Britain and Canada and the impact of culture on joint, combined and multi-national operations : a methodology for profiling national and organisational cultural values and assessing their influence in the international workplace
Bibliographic record
Abstract
This study identifies the influence of national and military organisational values on the cultures of the armed forces of Australia, Britain and Canada, in order to assess the impact of culture on Joint, Combined and Multinational operations. This is achieved by: · Defining culture, values and related concepts. · Outlining a viable methodology to examine and profile cultural values. · Demonstrating why values form the basis of this study. · Reviewing the body of cross-cultural academic literature on cultural values and the military. · Executing a measurement of values in a consistent and academically sound manner. · Examining national influences on the culture of the armed forces of Australia, Britain and Canada. · Examining intra- national organisational influences on the culture of the services of the armed forces of Australia, Britain and Canada. · Examining international organisational influences on the culture of the services of the armed forces of Australia, Britain and Canada. · Focusing on the values of the armed forces examined in this study in order to compare the findings with the results obtained from the Values Survey Module. · Discussing the implications of the findings of this study and demonstrate how the values of the nations and organisations that have been examined can be expected to affect future operations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".