Bibliographic record
Abstract
Virtual worlds hold enormous promise for corporate education and training. From distributed collaboration that facilitates participation at a distance, to allowing trainees to experience dangerous situations first-hand without threat to personal safety, virtual worlds are a solution that offers benefits for a multitude of applications. While related to videogames, virtual worlds have different parameters of interaction that make them useful for specific location or open-ended instructional exchanges. Research suggests that participants identify quickly with roles and situations they encounter in virtual environments, that they experience virtual interactions as real events, and that those experiences carry over into real life. This paper will evaluate the attributes of a successful applied training project, the Canadian border simulation at Loyalist College, conducted in the virtual world Second Life. This simulated border crossing is used to teach port of entry interview skills to students at the college, whose test scores, engagement level, and motivation have increased substantially by utilizing this training environment. The positive results of this training experience led the Canadian Border Services Agency (CBSA) to pilot the border environment for agency recruits, with comparable results. By analyzing the various elements of this simulation, and examining the process with which it was used in the classroom, a set of best practices emerge that have wide applicability to corporate training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 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".