Open knowledge commons versus privatized gain in a fractured information ecology: lessons from COVID-19 for the future of sustainability
Bibliographic record
Abstract
Abstract COVID-19 has shone a bright light on a number of failings and weaknesses in how current economic models handle information and knowledge. Some of these are familiar issues that have long been understood but not acted upon effectively – for example, the danger that current systems of intellectual property and patent protection are actually inimical to delivering a cost-effective vaccine available to all, whereas treating knowledge as a commons and a public good is much more likely to deliver efficient outcomes for the entire global population. But COVID-19 has also demonstrated that traditional models of knowledge production and dissemination are failing us; scientific knowledge is becoming weaponized and hyper-partisan, and confidence in this knowledge is falling. We believe that the challenges that COVID-19 has exposed in the information economy and ecology will be of increasing applicability across the whole spectrum of sustainability; sustainability scholars and policymakers need to understand and grasp them now if we are to avoid contagion into other sectors due to the preventable errors that have marred the global response to COVID-19.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.063 |
| Scholarly communication | 0.028 | 0.041 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".