When Should We Care About Sustainability? Applying Human Security as the Decisive Criterion
Bibliographic record
Abstract
It seems intuitively clear that not all human endeavours warrant equal concern over the extent of their sustainability. This raises the question about what criteria might best serve for their prioritisation. We refute, on empirical and theoretical grounds, the counterclaim that sustainability should be of no concern regardless of the circumstances. Human security can serve as a source of criteria that are both widely shared and can be assessed in a reasonably objective manner. Using established classifications, we explore how four forms of sustainability (environmental, economic, social, and cultural) relate to the four pillars of human security (environmental, economic, sociopolitical, and health-related). Our findings, based on probable correlations, suggest that the criteria of human security allow for a reliable discrimination between relatively trivial incidences of unsustainable behavior and those that warrant widely shared serious concern. They also confirm that certain sources of human insecurity, such as poverty or violent conflict, tend to perpetuate unsustainable behavior, a useful consideration for the design of development initiatives. Considering that human security enjoys wide and increasing political support among the international community, it is to be hoped that by publicizing the close correlation between human security and sustainability greater attention will be paid to the latter and to its careful definition.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".