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Record W3214991896 · doi:10.29173/pandpr29500

Language-ing the Earth: Experiential Renewal for a Relationally Sensitive Environmentalism

2021· article· en· W3214991896 on OpenAlexaffvenue
Patrick Howard

Bibliographic record

VenuePhenomenology & Practice · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsCape Breton University
Fundersnot available
KeywordsExperiential learningConceptualizationPsychologyEnvironmentalismPhenomenology (philosophy)ObjectificationEpistemologyNatural (archaeology)AestheticsSocial psychologyEnvironmental ethicsSociologyPolitical sciencePhilosophyGeography

Abstract

fetched live from OpenAlex

This paper investigates human relationship with the larger living landscape that is grounded in experiential renewal. Phenomenology is antithesis to the process of abstraction and objectification through which the world as we experience it is diminished by conceptualization and categorization. Recent studies to understand the natural world as a hermeneutic text offers important reflections on the human mediation of the meaning of the more-than-human-world and assists in understanding the implications of our encounters with the world. Phenomenology, however, is unique in its capacity to bring to expression, rather than silencing, our relationship with the natural world and our human inherency in it. This paper explores phenomenologically sensate reciprocity as it is encountered in lived experience. Through deepening our attunement for our embodied integration in a living world we may relearn and restore a capacity to dwell more thoughtfully with newfound sensitivity, respect, and restraint in the ecosystems on which we wholly depend.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.018
GPT teacher head0.275
Teacher spread0.257 · 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.

Study designBench or experimental
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

Citations0
Published2021
Admission routes2
Has abstractyes

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