On the superiority of the incremental costing approach in IP account of profits
Bibliographic record
Abstract
This article addresses the ‘deduction of expenses’ component of the account of profits remedy in intellectual property (IP) contexts. It aims at two primary objectives: 1) to distinguish the incremental costing approach from other competing costing approaches applied in the disgorgement process, particularly the proportional absorption approach with which is it often conceptually confused; and 2) to make a case for the superiority of the incremental costing approach over other competing ones, particularly in the area of IP. In pursuing this discussion, this paper relies on a cross-section of legal authorities and commentaries on account of profits, particularly relating to the IP context, across major Commonwealth legal jurisdictions (ie Australia, Canada and the UK). It also draws upon judicial decisions and academic discussions on the subject from the USA, particularly because of the sophistication in economic understanding exuded in the treatment of the subject.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| 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".