Selective border permeability: Governing complex environmental issues through and beyond COVID-19
Bibliographic record
Abstract
COVID-19 has changed the permeability of borders in transboundary environmental governance regimes. While borders have always been selectively permeable, the pandemic has reconfigured the nature of cross-border flows of people, natural resources, finances and technologies. This has altered the availability of spaces for enacting sustainability initiatives within and between countries. In Southeast Asia, national governments and businesses seeking to expedite economic recovery from the pandemic-induced recession have selectively re-opened borders by accelerating production and revitalizing agro-export growth. Widening regional inequities have also contributed to increased cross-border flows of illicit commodities, such as trafficked wildlife. At the same time, border restrictions under the exigencies of controlling the pandemic have led to a rolling back and scaling down of transboundary environmental agreements, regulations and programs, with important implications for environmental democracy, socio-ecological justice and sustainability. Drawing on evidence from Southeast Asia, the article assesses the policy challenges and opportunities posed by the shifting permeability of borders for organising and operationalising environmental activities at different scales of transboundary governance.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".