Bibliographic record
Abstract
Technology lies at the centre of the climate change debate and plays a pivotal role in addressing the global challenge of climate change and sustainable development in today’s economy. Access and timely diffusion of green technologies required for adaptation and mitigation are among the major challenges faced by the international community. The role of the patent system has become the subject of increased attention in climate change discussions on technology transfer. The core technology that should be disseminated with the patent is not easily accessible in practice or has little technical value. New mechanisms for collaborative innovation are required to foster the green technology sector. This chapter argues that green patent pledges can provide a new mechanism of collaboration and transferring green technology innovation, which can work within the existing intellectual property legal regime. The chapter examines the various forms of patent pledges related to green technology and their rationales by analysing three main models of green patent pledges: Eco-Patent Commons, GreenXchange and Canada’s Oil Sands Innovation Alliance (COSIA). The chapter concludes by suggesting a model legal framework for green patent pledges and calls for a global system to share green patents governed by an international body where accession rules are open to third parties based on fair, reasonable and non-discriminatory terms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".