Bibliographic record
Abstract
I felt an urge to create a piece that depicted the current state of our natural world using an analogy from medicine. The impact we have had on the natural environment has been like a disease. Current economies prioritize consumerism and expansion, congesting our world (the heart) with garbage and infecting our air, soil, and water with waste products. The belief we have a “right” to exploit the earth (right heart) has led to the rapid deterioration of what is “left” of the natural world (left heart). Like congestive heart failure, we currently have no single cure for climate change, but that doesn't mean we can't create solutions for the future. In the words of David Attenborough at the 2019 World Economic Forum: “We need to move beyond guilt or blame, and get on with the practical tasks at hand. If people can truly understand what is at stake, I believe they will give permission for business and governments to get on with the practical solutions. And as a species, we are expert problem solvers. but we’ve not yet applied ourself to this problem with the focus that it requires. We can create a world with clean air and water, unlimited energy, and fish stocks that will sustain us well into the future. But to do that, we need a plan.”
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.045 | 0.006 |
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".