“Caring for Home through Nature’s Rights” As we celebrate International Mother Earth Day, this Interactive Dialogue provides
Bibliographic record
Abstract
us the opportunity to examine the role of economics in furthering an ethical relationship between ourselves and the Earth. The root of “economics ” is from the Greek meaning to manage our home. How are we managing our home, from our closest connections with family and friends, outwards to our communities, nations and the planet as a whole? At all levels, we can do better. Our current economic system misguidedly assumes that infinite economic growth is possible on a finite planet, that wealth concentrated in the hands of a few benefits all, that more wealth brings more well-being, and that the natural world is a “resource ” for our use. As a result, Mother Earth, which sustains us, is visibly declining. Scientists estimate that because of our actions, a quarter of mammals and 40 % of amphibians may become extinct in the foreseeable future. This rate of extinction is 1,000 times the average across history. Climate change accelerates these impacts, creating a heightened urgency for action. The World Bank recently studied the potential impacts of a 4 o C temperature increase, an increasingly likely scenario. They found that this would create a “transition of the Earth’s ecosystems into a state unknown in human experience.”
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.038 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.016 | 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".