Global politics of the COVID-19 pandemic, and other current issues of environmental justice
Bibliographic record
Abstract
In 2020, the world was hit by COVID-19. Big data expeditiously travel around the world, having a performative effect on the way individuals, private enterprises, and local governments make decisions regarding the pandemic. As collective actions, symbolisms, and representations are (re)created or (re)constituted in the contexts of the pandemic, “new” questions can be formulated and “old” ones revisited regarding ecological justice in environmental education (research). Framing the special issue (SI) as an assemblage, we, as editors, challenged the authors to constantly return to the question of “What is in it for Nature?”, while presenting their findings on what pandemics reveal about the politics of global environmental issues. As individual contributions, each paper of the SI targets a particular context of the pandemic to (re)visit environmental (in)justice. As an assemblage, the SI assesses where we, as an international community, currently stand in relation to “new” and “old” issues of environmental justice.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".