Proposing an Examen for Living the Ecology of Daily Life and Building a Culture of Care
Bibliographic record
Abstract
This article examines a relatively unexplored aspect of integral ecology in Laudato Si’ called “the ecology of daily life” and considers how living a healthy ecology of daily life relates to the unique vocation of humans to care for creation. Specifically, what does the Pope intend by “the ecology of daily life”? What are some obstacles to living it? How can living the ecology of daily life help build a culture of care? Based upon the principles articulated in the encyclical, the article proposes an examen for assessing progress in living the ecology of daily life. This examen is applied to two case studies in order to discern a fruitful practice of the ecology of daily life. The case studies represent environmental situations that, while affected by larger scale industrial/commercial processes, are primarily driven by micro-scale decision-making and small daily actions of individuals and local communities. The first case study focuses on endocrine disrupting chemicals as an example of a polluted ecology of daily life, and the second highlights a zero waste initiative as an exemplar of an integral ecology of daily life. The article concludes with comments on lessons learned from the exercise of applying the examen to two concrete situations. This approach can help individuals and communities discern how to build a culture of care based upon the principles of the ecology of daily life as they are presented in Laudato Si’.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 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".