Is It Time to Shift Our Environmental Thinking? A Perspective on Barriers and Opportunities to Change
Bibliographic record
Abstract
In 2015, the United Nations General Assembly unanimously adopted the 2030 Agenda for Sustainable Development and Sustainable Development Goals. In 2019, the release of the global assessment report of the United Nations’ Intergovernmental Platform on Biodiversity and Ecosystem Services unfortunately demonstrated that our planet may be in more trouble than expected. The main drivers have been identified for many years and relate to human activities such as over-exploitation of natural resources leading to land degradation, deforestation, ocean and atmospheric pollution, and climate change. Despite international agreements and conventions, we are gradually reaching the planet’s boundaries. In this commentary, we present an analysis of the current worldview, discuss the humanist roots of this view, and the barriers to be able to move forward with the transformative changes that are needed for sustainability. We suggest that for these transformative changes to happen, there is a need to reconnect humans with nature, and we propose that some solutions could be devised in areas like education and social media. Changing our mindsets and worldviews are the most urgent courses of action we must undertake to avoid the inevitable.
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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.067 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.022 |
| 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".