Mentoring First-Year Distance Education Students in Taxation Studies
Bibliographic record
Abstract
This article introduces and reviews the experience of video-conference teaching in a comparative Indigenous law course taught by a team of legal colleagues. This teaching team delivers an internationally comparative Indigenous rights course to law students in Canada, the United States, Aotearoa/New Zealand and Australia simultaneously via fully interactive live video-conferencing technology. The international universities currently involved include: University of Ottawa, University of Saskatchewan, University of Oklahoma, University of Auckland, Monash University and the University of Queensland. Situated in six sites in different parts of the globe and in various time zones, this team teaches together to discover just how much their countries have in common in relation to Indigenous issues. Not only does the course explore similarities and differences in the experiences of the four jurisdictions but it also challenges both students and teachers to understand why those differences have occurred. The article focuses on two significant aspects of this course: first, the dynamics and logistics of teaching and delivering a course through video-conferencing to a number of global sites; and secondly, an analysis of the benefits and advantages of an internationally comparative Indigenous law course. It aims to enable other law teachers to consider the suitability of video-conferencing for international and comparative areas of legal study and for others to learn from the experiences of this team in relation to the benefits and difficulties involved in this teaching mode.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".