Bibliographic record
Abstract
Abstract China has achieved unprecedented economic growth in the past decades. This has had serious consequences on the environment and public health. The Chinese government now realizes that it is not just the quantity, but the quality of development that matters. It has begun to instigate a series of policies to tackle pollution, increase the proportion of clean energy, and redress the balance between urban and rural development—in a coordinated effort to build a harmonious society. Building a harmonious world was also the theme of the 33rd International Geographical Congress, which was held in Beijing last August. At the meeting, Bojie Fu, a member of National Science Review’s editorial board, shared a platform with geographers from Australia, China, Canada and France to discuss the challenges of urbanization, the roles of geographers in sustainable development, as well as the importance of food security, safety and diversity. Dadao Lu Economic geographer at the Institute of Geography and Natural Resources Research, Chinese Academy of Sciences, Beijing Jean-Robert Pitte Historical and cultural geographer at the University of Paris-Sorbonne in Paris, France Mark Rosenberg Health geographer at Queen's University in Ontario, Canada Mark Stafford Smith Ecologist at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) in Canberra, Australia Bojie Fu (Chair) Physical geographer at the Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing; President of Geographical Society of China
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".