Feist Goes Global: A Comparative Analysis Of The Notion Of Originality In Copyright Law
Bibliographic record
Abstract
The 1991 U.S. Supreme Court decision in Feist Publications v. Rural Telephone Service Company, Inc. delivered was hailed both as a landmark decision and a legal bomb. Was Feist so original as to deserve all the attention? After all, it did not establish a new originality paradigm as such but only ended a long division among federal circuits concerning the protection under copyright of factual compilations. A number of circuits had adopted a test similar to the one articulated in Feist (i.e., based on creative selection), while others required only evidence of labor, a test known as sweat of the brow. In reality, Feist did much more than resolve a definitional tension: it determined that there was a constitutional requirement of creativity. According to the U.S. database industry, the sky had fallen: factual compilations would no longer be protected and without adequate protection, investments necessary for the creation and maintenance of databases would dry up. That did not happen, even though debates concerning a federal tort of misappropriation continue. The purpose of this article is not to analyze whether Feist was correctly decided, but rather to show that a Feist like standard is now applied or may soon emerge in key common law countries. Moreover, in a move that may bridge the gap between the two major systems of copyright, we will demonstrate that civil law systems have also adopted a similar doctrine.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.034 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".