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Record W3008241804 · doi:10.1080/09669582.2020.1730387

Megaliths, material engagement, and the atmospherics of neo-lithic ethics: presage for the end(s) of tourism

2020· article· en· W3008241804 on OpenAlexaff
Mick Smith, Siobhan Speiran, Peter A. Graham

Bibliographic record

VenueJournal of Sustainable Tourism · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsConcordia UniversityQueen's University
Fundersnot available
KeywordsTourismSociologyAgency (philosophy)Environmental ethicsAnthropoceneTransformative learningFraming (construction)AestheticsEpistemologyArchaeologySocial scienceHistoryPhilosophy

Abstract

fetched live from OpenAlex

Climate change spells the end of tourism, and tourism is just one, and one of the least important, things to be so very near its end. This situation has emerged because of the global dominance of forms of material engagement that completely misunderstand the distribution of agency in the world. Framing the world as just an external human ‘resource’ diminishes the many forms of agency that elude, transcend, effect, facilitate and subvert human intentions. Even archetypically inanimate objects, such as stones, are actively constitutive of worlds, thoughts, and lives, including human lives, in myriad transformative ways. This lithic agency is illustrated in terms of the attraction that draws some to visit stone circles, and the atmospheres created by, and felt in, such places. This rather different understanding of a ‘visitor attraction’ and its ontological and ethical implications exposes some of the inadequate presumptions of our dominant form of life and its destructive atmospheric consequences at local and global scales. The paper employs Material Engagement Theory (MET), originating in archaeology and anthropology, as a way to understand the differences between the reductive assumptions of the Anthropocene and the redemptive possibilities of what it terms a neo-lithic ethics.

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
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 science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.321
Teacher spread0.282 · 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 designQualitative
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

Citations8
Published2020
Admission routes1
Has abstractyes

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