Slackers, Shirkers and Career-Changers: Imputing Income for Under/Unemployment
Bibliographic record
Abstract
A reader of Canadian appellate jurisprudence will think [imputing income] is a closed issue. After all, most Canadian appeal courts have concluded that there is no need to prove "bad faith" or "a specific intent to evade child support obligations" in order to impute income under s 19(1)(a): Nova Scotia, Manitoba, Ontario, and British Columbia. So far, only Alberta has adopted the "bad faith" test. Under a slightly-different regime, the Quebec Court of Appeal appears to have rejected a bad faith test, opting for a more flexible approach too. In a recent New Brunswick Court of Appeal decision, there is a hint that they too would join with the "reasonableness" crowd. Lower courts are divided, but the trend is clear, you could say. The Supreme court of Canada has not yet spoken on this subject.\nSo the consensus legal test is one of "reasonableness." The legal problem is resolved. All the rest is just about facts, right? Well, facts and case-by-case discretion.\nI will argue that we've only just begun, to quote the Carpenters. The "reasonableness" test solves the first and easiest issue under s 19(1)(a). A review of the case law reveals that the real differences are not about "facts," but about "policy." More appellate guidance is required. "Rules" may or may not be possible, but the policy issues need to be clearly stated. This article attempts to begin that next step.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".