Socio-Cultural Considerations in International Geomatics Training
Bibliographic record
Abstract
The perception of science and scientific findings can vary significantly between different cultures. In order to meaning fully convey scientific and technical information to international audiences, particularly in a training context, an appreciation of cross-cultural communication differences is essential. This paper is derived from a curriculum developed by the Training and Technology Transfer Section (TTTS) of the Canada Centre for Remote Sensing for trainers and scientist/trainers who are new to international projects. The TTTS curriculum is directed at improving the delivery of geomatics training to different countries and cultures. It places primary emphasis on socio-cultural considerations, as they relate to effective cross-cultural training and technology transfer. The discussion includes measures of effectiveness of such training and elements of culture that have the greatest effect on learning. The concepts of adult learning are also discussed. Based on the TTTS experience and that of other colleagues from CCRS and elsewhere, this paper provides ideas for geomatics specialists who will find themselves doing double duty as applications specialists and trainers in the international environment. To illustrate the complexity and diversity of international training, references are made to materials in the workshop, such as field-proven models, examples and anecdotal information. Though oriented towards geomatics, the workshop curriculum outlined in the paper may be extended to other training situations involving complex technology transfer and the goal of sustainable application.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".