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Record W2792895821 · doi:10.5840/envirophil201822660

In Defense of the Human Difference

2018· article· en· W2792895821 on OpenAlexvenueno aff
Sean McGrath

Bibliographic record

VenueEnvironmental Philosophy · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsHumanityHumanismEpistemologyEnvironmental ethicsConsciousnessAnthropocentrismPhilosophyValue (mathematics)CriticismFalsityHuman spiritIntrinsic value (animal ethics)SociologyLawPolitical science

Abstract

fetched live from OpenAlex

Against the prevalent trend in eco-criticism which is to deny the human difference, I summon a set of untimely tropes from metaphysics in the interest of advancing an ecological humanism: the difference in kind between human consciousness and animal sensibility; the uniquely human capacity for moral discernment; and the human being’s peculiar freedom from the material conditions of existence. While I agree with eco-critics who argue that anthropocenic nature is not only finite, but sick: sickened by our abuse and neglect, I disagree that this abuse is simply a result of insisting on the human difference (“anthropocentrism”), nor is species egalitarianism the way forward. On the contrary, the eco-collapse, referred to as the sixth great extinction event, is the consequence of a general disavowal of the human’s special call to take responsibility for the relation between the human and the non-human, and only a re-awakening of this responsibility can restore health to anthropocenic nature. The non-human cannot effect this restoration, for that is not its vocation. A difference in vocation is not necessarily a difference in moral worth, and so the human difference does not justify denying the “intrinsic value” of the non-human. Humanity is uniquely responsible both for the mess we are in and for cleaning it up.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score1.000

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 studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.230
Teacher spread0.212 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2018
Admission routes1
Has abstractyes

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