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Record W2533705414 · doi:10.1163/15685357-02002100

Overcoming Fear, Denial, Myopia, and Paralysis

2016· article· en· W2533705414 on OpenAlexaff
Mark D. Hathaway

Bibliographic record

VenueWorldviews Global Religions Culture and Ecology · 2016
Typearticle
Languageen
FieldPsychology
TopicParanormal Experiences and Beliefs
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDenialAction (physics)PsychologyBuddhismPoliticsSet (abstract data type)HumanitySocial psychologyPerceptionPsychological resilienceAestheticsEnvironmental ethicsSociologyPsychoanalysisPolitical scienceHistoryLawNeuroscience

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.678
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.310
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2016
Admission routes1
Has abstractyes

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