The Centre for InterculturalLearning of the CanadianForeign Service Institute is thelargest organizer of cross-cul-
Bibliographic record
Abstract
tural and international training programs in Canada for outgoing government and private-sector workers. Four years ago, the Centre embarked on a process to review and redesign its training curricu-lum and evaluation systems, with the ultimate aim of expanding the systems to cover personnel selection for overseas assignments and performance monitor-ing after arrival. Prior to the redesign process, several weaknesses of our training programs, and of most other cross-cultural training pro-grams, were identified. In the first place, there was an inconsistency of content, as much of the course design depended on the preferences of individual trainers. Second, training design was somewhat incoherent, that is, not sufficiently based on a theory or set of empirical generaliza-tions about what makes for a successful cross-cultural worker. In other words, a thorough competency analysis of inter-cultural effectiveness had never been done. Third, while a consistent core cur-riculum is desirable, there was not suffi-cient customization to individual needs. Finally, although the programs were in some loose way using a competency-based approach (some general notions of what constituted successful performance certainly existed), they were not easily evaluable in the sense of having precise and observable definitions of the expected results of the training once the trainee had been overseas for some time.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.057 | 0.007 |
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".