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Record W7155523929 · doi:10.7202/1124393ar

Landscape, Time and the Philosophy of Michel Serres

2025· article· en· W7155523929 on OpenAlexvenueno aff
James Kelly

Bibliographic record

VenueThe Trumpeter · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsSubject (documents)MetaphorFace (sociological concept)Relevance (law)GermanContinental philosophyAssemblage (archaeology)Foundation (evidence)Interpretation (philosophy)

Abstract

fetched live from OpenAlex

This article explores the changing uses of the word landscape over time, using ideas from the philosophy of Michel Serres to examine the word’s relevance to contemporary debates on humans and nature. It begins by reflecting on the three types of domestication of the landscape (the farm, the garden, and the national park) identified in Serres’s introductory essay to a book to commemorate the fortieth anniversary of the Pyrenees National Park. These three figures provide a point of departure for an exploration of how the changing meanings of the word “landscape” in the English language reflect our position as humans with respect to nature. Using the geological metaphor of the stratigraphic column to look at different strata in the word’s history and the different meanings associated with it over time, the first part of the article examines how a specific usage of the word associated with each stratum reflects a certain way of seeing the world and the human position in it. Having thus grounded the term etymologically, the second part of the article examines how ideas from Serres’s The Incandescent embody a fundamental shift in how the human subject is conceived, providing a philosophical foundation for a new way of thinking about landscapes, advocating the importance of the concept for reflecting on the ecological challenges we face in the twenty-first century.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.698

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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 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
Published2025
Admission routes1
Has abstractyes

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