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Record W2288448472 · doi:10.4172/2252-5211.1000154

Wasting and Recovering Time: The Golf Course as Shangri-La

2014· article· en· W2288448472 on OpenAlexaboutno aff
Randy Schroeder

Bibliographic record

VenueInternational Journal of Waste Resources · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Philosophy and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsCourse (navigation)WastingMass wastingHistoryOperations managementAeronauticsEngineeringMedicineGeotechnical engineering

Abstract

fetched live from OpenAlex

I have a friend who critiques my urban lifestyle. He lives in a golfcourse suburb, or, as he puts it, “nature.” While his position is easy to mock, more interesting is the puzzle his subjectivity and mine present: what kind of representational apparatus would account for his vision of nature and my mockery of his vision? Do we balance each other on a semiotic teeter-totter? Intrigued, I carried out a “phenomenological” experiment one Sunday afternoon by driving through a Calgary golf exurb named Elbow Valley, a neighborhood in a gorgeous section of foothills, well treed, designed with views and seamed with parklets and pathways. For the first ten minutes I marveled, and thought about complexity and integration; after ten I found everything tedious. I couldn’t discount the cliched sense that I was touring a film set or section of Disneyland. Something felt missing-not absent, but attenuated, like an imbalance between order and turbulence. Surely this landscape, like all others, was haunted by excess. But the repressions felt so strong that I doubted there was any chance of genuine novelty or creative advance ever arising. Of course, my experiment was really anything but phenomenological, since I could not remotely bracket the local; indeed, the local-from the architecture to my preconceptions—suffused my consciousness. How much did it cost to live in this nature, so carefully bundled around and into this golf course? How did the landscape loan its meanings to the course, and the course, in turn, to the exurb, in a cycle both centrifugal and centripetal?

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.372

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.223
Teacher spread0.218 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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