The Precautionary Principle and its Application in the Intellectual Property Context: Towards a Public Domain Impact Assessment
Bibliographic record
Abstract
This chapter considers whether the precautionary principle - a central element of contemporary environmental law and policy - can be usefully applied in the intellectual property context as a means through which the public domain can be protected. Assuming the importance of the public domain, and arguing that expansions in intellectual property protection risk harming the public domain, this chapter contends that it is appropriate to apply the precautionary principle in the intellectual property context in order to guard against harm to the public domain; suggests several ways in which a precautionary principle (or a precautionary approach) could be applied in the intellectual property context; and considers one possible instantiation of the precautionary principle in the context of intellectual property reform, namely in the form of a Public Domain Impact Assessment (PDIA). Modeled on the Canadian Environmental Assessment Act, the PDIA is envisioned as a process through which proposals for intellectual property reform, prior to their enactment, are evaluated by an independent review panel in order to determine their potential impact on the public domain.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".