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Record W6889428095 · doi:10.25623/conn033-crover-1

Kinship and the River Cam: George Herbert’s Anthropocentrism Reconsidered

2024· article· en· W6889428095 on OpenAlexaff

Bibliographic record

VenueEnglisches Seminar der Universität Tübingen · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Crisis of the 21st Century
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsAnthropocentrismHierarchyGeorge (robot)Agency (philosophy)Natural (archaeology)Reading (process)EcocriticismKinship

Abstract

fetched live from OpenAlex

George Herbert’s devotional poetry, with its minute attention to the natural world, ought to be well suited to early modern scholars with an ecocritical bent. However, his work is frequently dismissed as disappointingly anthropocentric or simply as a poor fit for ecological readings of the early modern literary canon. With a few exceptions, a more egalitarian reading of Herbert’s engagement with nature has been largely resisted. This article aims to address this lack and reexamine Herbert’s relationship with nature. I read Herbert’s writing as revealing an investment in a flatter ontological hierarchy than he is usually given credit for. While the debate about how much agency Herbert is willing to ascribe to the nonhuman in his poetry continues, little time has been spent comparing this work with his engagement with the natural world in his prose letters. Specifically, four Latin letters protesting the proposed drainage of the River Cam in 1620 merit more attention than they have received in this debate and may help, I suggest, clarify his position since they provide insight into how he applied his thinking in practice, not just in theory. Ultimately, Herbert’s anthropocentric engagement with the natural world is nuanced by his figuring of the relationship between humans and nature as one of familial kinship.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.014
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.183
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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