Intellectual Property Futures: Exploring the Global Landscape of IP Law and Policy
Bibliographic record
Abstract
The past few decades have been witness to a number of important developments with respect to the global intellectual property (IP) system, including shifts in focus between multilateralism and bilateralism/regionalism; growing recognition of the various ways in which IP intersects with and impacts areas including human rights, development, trade, and social justice; broad acknowledgement of the economic value of many IP rights; and important theoretical interventions that have challenged the values underlying the global IP system. These developments have occurred alongside several other events, changes, and crises that have altered the landscape of our global communities. Chief among them are climate change; armed conflicts; the COVID-19 pandemic; economic changes to work; technological shifts including those relating to the internet and artificial intelligence, and their role in society; and growing recognition of the inequities that exist within and between societies as well as the ways in which these inequities are reinforced and maintained through systemic discrimination and ongoing colonialism. Given these developments, changes, and crises, what is the future of IP law and policy? Featuring contributions from scholars from across Canada and around the world, this collection offers insights into eighteen possible futures for the global IP system. Collectively, these chapters re-envision international agreements; rethink Canadian IP law; argue for the creation of space for Indigenous legal traditions; highlight the promises and perils of technology as it relates to IP; expose inequities and injustices, and provide possible pathways to correct them.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.037 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".