Environmental sustainability and quality of life: from theory to practice
Bibliographic record
Abstract
The term Quality of Life (QoL) has been widely used in a number of disciplines to express the idea of personal well-being in a framework, which goes beyond the simple economic equation of well-being (SWB) with income.The results of numerous studies reveal that (1) objective well-being may be compatible with environmental sustainability, often due to synergies arising in terms of reduced pollution and health benefits; (2) well-being and environmental sustainability may be incompatible with one another if well-being is defined as the satisfaction of preferences, psychological well-being and/or subjective well-being (SWB).One possible way of better constructing both concepts of environmental sustainability and well-being is by linking them to individual mental maps (models).Mental maps are those core beliefs that may explain how individuals select and process information in interpreting life events, and may account for individual differences in these interpretations.We argue that the prerequisite and basic mechanism for both environmental sustainability and well-being is the mental sustainability of these internal working models.We define mental sustainability an optimal balance between social adaptability and individual authenticity of a person.The current study is the first stage of the interdisciplinary project focusing on clarifying approaches to, and relationships between, subjective well-being and environmental sustainability.The aim of this study is to apply our operationalisation of SWB for investigating what personality factors are responsible for consumerism (consumption satisfaction).Our results revealed that people with significantly different levels of consumption satisfaction also had significantly different levels of SWB as well as of all other personality variables under consideration.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".