Debunking the Fair Use vs. Fair Dealing Myth: Have We Had Fair Use All Along?
Bibliographic record
Abstract
Eleven decades ago, on December 16, 1911, the Imperial Copyright Act of 1911 received royal assent, codifying fair dealing for the first time, and thus explicitly recognizing it, in the imperial copyright legislation. Ten years later, the same fair dealing provision would appear in the Canadian Copyright Act and would remain the basis of the current fair dealing provisions. Tragically, what was supposed to be an exercise in the codification of a dynamic and evolving common law principle, usually referred to as “fair use,” ended up – with a few notable exceptions – in a hundred years of solitude and stagnation. Misinterpreting the 1911 Act, some courts and commentators in the UK and other Commonwealth countries adopted a narrow and restrictive view of fair dealing. Meanwhile, in the United States, fair use, the same common law concept that English and American courts developed, remained uncodified for most of the twentieth century. When the United States finally codified fair use in 1976, Congress left no doubt that the codification would not alter its common law basis and ought not hinder its flexibility and adaptability. Thus, toward the end of the twentieth century, a noticeable split in Anglo-American copyright law emerged: an open, flexible, and general fair use regime in the United States, and a seemingly rigid and restrictive fair dealing tradition in the Commonwealth countries.
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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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.143 |
| Scholarly communication | 0.025 | 0.059 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.019 | 0.043 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".