Open for Climate Justice: Intellectual Property, Human Rights, and Climate Change
Bibliographic record
Abstract
This commentary highlights the history of conflict and division over intellectual property, technology transfer, and clean technologies during international climate negotiations. It considers the scope for open licensing to make environmental knowledge, data, technology, and intellectual property more widely accessible and available in order to better address climate adaptation and mitigation, as well as loss and damage. There has been a renewed interest in open access models for climate research, knowledge, and data. Creative Commons, SPARC and EIFL have launched a 4 year Open Climate Campaign, with funding from the Arcadia Foundation. The theme for the Open Access Week 2022 is Open for Climate Justice. Patent pledges have become increasingly popular as a means of sharing technologies. The Low Carbon Patent Pledge was launched in 2021 by Hewlett Packard Enterprise, Microsoft, and Facebook. The Pledge is designed to help disseminate clean technologies, subject to intellectual property rights. Meanwhile, the Government of Canada has experimented with patent collectives in the field of clean technologies. Open government policies have increasingly focused upon climate data, knowledge, and technologies. The Biden administration has been seeking to promote open innovation in the United States. At an international level, there has been discussion about the relationship between open science and human rights. The UNESCO Recommendation on Open Science provides a framework for the further development of policies in this field. While such new open source projects have promise, there is a need to scale up initiatives on open access, open data, open science, and open innovation to better address the challenges of climate change, biodiversity loss, and sustainable development.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.040 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 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".