Comparing Sustainability to a Good Life and Well-Being: Overlap, Differentiations and Indefinite Overlap
Bibliographic record
Abstract
In our society, sustainability has emerged as a major concept in our daily lives and activities, e.g. from reducing the environmental impact of our foods to corporate social responsibility in doing business, and social impact of our activities. The original idea of sustainability was to address human development within social, ecological, and economic boundaries. Nowadays, however, sustainability is more and more extended to other areas of our lives, including aspects of a good life and well-being. The aim here was to compare sustainability, a good life, and well-being and determine their overlap, differentiations, and indefinite or undecided overlap when considering the original definitions. Following from the definition of sustainability, a good life, and well-being, I analyze the overlap, differentiations, and indefinite overlap of these concepts. With this comparison, I show that sustainability is clearly adapting to include more aspects of a good life and well-being (approximately 26% overlap), but is limited to do so from its original definition. I conclude that overlap between concepts exists and by being relatively different they are fundamentally supportive to one another and need to be applied accordingly to further support sustainable development.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".