Open knowledge commons versus privatized gain in a fractured information ecology: lessons from COVID-19 for the future of sustainability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it