Tackling Environmental Problems: Are People and the Environment Antithetical?
Bibliographic record
Abstract
In the era where human communities have been plunged into unprecedented environmental problems, scientists and policymakers have been forced to revisit and reflect on the relationship between humanity and the natural environment. In light of all these developments, fundamental questions have been asked, such as, should nature be left alone? Are humans separate from nature? Is it too late to turn back the clock? How can we tackle the climate crisis? At the core of these questions lies the issue of the human-environment relationship, with humans being both dependent on and simultaneously harming the environment. Although the dependence of humans on natural systems is acknowledged, there seems to be uncertainty about balancing human well-being, ecosystem, and environmental integrity. It appears as though these three factors cannot co-exist harmoniously. In this contribution, we discuss the axioms of the environment and humanity and extract lessons that can be used to address the increased environmental concerns that have challenged the world. We also present a rationale for using an interdisciplinary, holistic approach to address environmental problems, proposing a Nature-integrated in Whole Systems Framework. We argue that environmental problems cannot be successfully addressed without incorporating human dimensions and treating systems as wholes. We base our argument on the fact that the challenges facing humanity are so intertwined that addressing one issue without considering the others is futile. We propose that we need to integrate nature into every aspect of life.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.018 | 0.040 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.012 | 0.013 |
| 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".