ELECTRONIC FILING: ASSESSING ITS FEASIBILITY FOR TRADE DISPUTES ADMINISTERED BY THE NAFTA SECRETARIAT An Analysis of the Issues Involved in the Successful Implementation of Electronic Filing
Bibliographic record
Abstract
This research paper would have been somewhat more of the challenge it already was without the support, encouragement, and involvement of some key people. Undertakings of this magnitude require the assistance of individuals who can offer a different perspective and distinct knowledge to the process and I would like to offer my gratitude to all of them. More specifically, my appreciation goes out to Dr. Geoff Gallas for taking the time early on to ensure that I was headed in the right direction and to ask the questions that would help realign my ideas when necessary. Special thanks to Mr. Michel Cailloux, from the University of Ottawa who took time from his busy schedule to review, more than once, this paper and to offer valuable suggestions and precious guidance on the content and structure. Part of his advice was to warn me that it was his obligation to question my thinking and that at times, I might actually start to dislike him. We are still on speaking terms today. I am also indebted to Mr. Gerry Amiot, friend and relative; a busy father/teacher who was generous enough to proofread this paper and ensure it was readable. I would like to thank the NAFTA panel members and counsel who responded to the survey. Their responses provided interesting data and comments for the benefit of this study and
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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 | 0.119 | 0.379 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".