Bibliographic record
Abstract
Drawing on insights from neuroscience, psychology, Buddhism, and the Beatitudes of Jesus, this paper explores the role emotions play in influencing human responses to the ecological crisis. While political, technological, and economic factors contributing to this crisis are often analyzed, emotional factors tend to be neglected or underestimated. Humans may be suffering from a condition analogous to the “myopia for the future” described by Antonio Damasio which impedes both our perception of the crisis and our response to it. Traditional Buddhist psychology’s analysis of the “three poisons” provides helpful insights into why humans may fail to respond to distressing information. At the same time, emotions have the potential to empower humanity to overcome the interwoven dynamics of denial, despair, and addiction and to facilitate a collective response to the ecological crisis. Joanna Macy has developed an integrated set of interactive, spiritual practices to enable persons to reconnect emotionally to the entire Earth community, overcome both despair and myopia for the future, and take meaningful action to heal the world. The Aramaic version of Matthew’s Beatitudes as interpreted by Neil Douglas-Klotz also models a spiritual process for overcoming despair by working with and through emotions to empower restorative action.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".