Creating a Transnational Green Knowledge Commons for a Socially Just Sustainability Transition
Bibliographic record
Abstract
Humanity faces numerous deeply interconnected systemic risks to sustainability—a global polycrisis. We need economic institutions that produce the knowledge required to address this polycrisis at the lowest cost, maximize the benefits that knowledge generates, and distribute those benefits fairly. Knowledge improves through use; its value is maximized when it is freely available. Intellectual property rights (IPRs), a form of monopoly, direct knowledge production towards market goods, raise the cost of doing research, and reduce the benefits by price-rationing access. Building on theories of the commons, the anticommons, and market failures, we propose the creation of a transnational green knowledge commons (TGKC) in which all knowledge that contributes to solving the polycrisis be made open access on the condition that any subsequent improvements also be open access. We argue that a TGKC is more sustainable, just, and efficient than restrictive IPRs and well suited to the motivations and governance institutions of public universities. We show how a single university could initiate the process and estimate that the cost would be more than offset by reduced IPR expenses. A TGKC would reduce the costs of generating and disseminating knowledge directed towards a sustainable future and help stimulate the transnational cooperation, reciprocity, and trust required for sustainable management of the global biophysical commons.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".