Stimulating <scp>post‐COVID</scp>‐19 green recovery by investing in ecological restoration
Bibliographic record
Abstract
In the face of the global COVID-19 recession, countries are looking at stimulus packages to kick-start their stalled economies. The recovery from the COVID-19 crisis also coincides with a critical opportunity to fight against ecosystem degradation and climate change. In this opinion article, I put in perspective that by investing in ecological restoration, governments do not have to choose between economic priorities and environmental concerns. First, I describe the restoration economy and give real-world examples of how investing in restoration activities can simultaneously ease pressure on the environment and create immediate jobs and revenues. Then I suggest that to obtain political attraction, a successful restoration strategy will require a triple-bottom-line approach to ensure that in addition to environmental objectives, stakeholders integrate socioeconomic outcomes in decision-making. Finally, I conclude that a new economic approach that prioritizes investment in our ecological capital will necessitate transdisciplinary policies to build bridges across the different silos of the economy and the environment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".