Bibliographic record
Abstract
M eetings this month of the World Parks Congress and the World Forestry Congress have all the trappings of any modern international conference on the environment. Experts will converge in Durban, South Africa, and Quebec City, respectively, to discuss the state of the world's parks and forests, in search of comprehensive strategies that will enable forests, and the diversity of life they support, to flourish for future generations. What is missing are the authority and accountability to ensure that those plans are carried out. The goals of the World Parks Congress are certainly laudable. Large parks protect species that are sensitive to human activities and provide a relative baseline with which to compare less intact ecosystems. And they provide a host of other natural services, from carbon sequestration to water filtration and soil protection. But parks alone are not enough to maintain a healthy planet. Last year, Brazil created the world's largest tropical park—a fantastic achievement. But this year deforestation in the Amazon has increased by 40%. If protecting one area merely shifts logging pressure to unprotected areas (the “waterbed effect”), then the overall value of the park is diminished. For example, China may have curtailed logging in some of its forests, but the Chinese demand for timber has not waned: It is now the world's second largest importer of wood, which is having a devastating effect on other parts of Asia. A true model of sustainability will incorporate protection at all scales, from large regions, to landscapes, to individual sites where development occurs. This means that forestry must switch from its current focus on resource extraction—what to take—to ecosystem-based management—what to leave. Canada's boreal forest is a prime example of a region with tremendous potential for conservation. It is one of just three regions on Earth (the others being the Amazon and the Congo) where large, unfragmented, relatively pristine forests still exist. A solid ecosystem-based strategy could weave together conservation needs with First World concerns and resource opportunities. But having a strategy is just the first of three critical components that are needed for conservation to succeed. Success also requires authority and accountability. Right now, most well-intended conservation measures start and end with an action plan. Scientists have been charged with the responsibility to find ways for a growing human population to live within the limits of our ecosystems, yet the authority to see that those plans are carried out rests in other hands. Politicians do have the authority, but rarely are they in power long enough to experience the fruits or faults of their actions. What's more, as soon as a politician is elected, he or she begins campaigning to get reelected in a few short years. That means politicians are focused, like industry, on maximizing short-term profits and employment, even though investments in protection yield far superior returns in the long run. It has been estimated that the annual cost of maintaining a worldwide network of nature reserves covering 15% of our land and sea areas would be about $45 billion. But the natural services these areas provide every year are collectively estimated to be worth 100 times that amount ( Science , 9 August 2002, p. [950][1]). Recommendations from international science conferences are too easily ignored. Groups of scientists can spend years collaborating on plans to solve environmental problems, only to have their recommendations shelved by government. So how do we hold politicians accountable? Only the public truly has that power. Unfortunately, the public receives science messages in a disjointed and disconnected way. Scientists are trained in narrow disciplines; a forest biologist might be loath to speak out about the possible connection between climate change and the forest fires that have ravaged parts of Canada and Europe this summer. As a result, the public continues to see nature as something “out there” that is distinct from humanity, outside the realm of public policy and impossible to change. To engage the populace, scientists have to go beyond their narrow roles as experts to become leaders who go public with their concerns. It's the only way to ensure that those with the authority to carry out conservation plans are held accountable for their actions. [1]: /lookup/doi/10.1126/science.1073947
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.083 | 0.015 |
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".