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Record W4367051284 · doi:10.1017/s0068245423000023

MAPPING THE LEIGH FERMORS’ JOURNEY THROUGH THE DEEP MANI IN 1951

2023· article· en· W4367051284 on OpenAlexafffund
Rebecca M. Seifried, Chelsea A.M. Gardner, Maria Tatum

Bibliographic record

VenueThe Annual of the British School at Athens · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsAcadia University
FundersUniversity of Massachusetts AmherstUniversity of British ColumbiaUniversity of Hawai'iKillam TrustsSocial Sciences and Humanities Research Council of CanadaAcadia UniversityMount Allison UniversityArchaeological Institute of America
KeywordsCartographyKey (lock)GeographyHistoryArchaeologyOperations researchComputer scienceEngineering

Abstract

fetched live from OpenAlex

In the summer of 2019, members of the CARTography Project set out to re-create the route that Patrick and Joan Leigh Fermor took during their first visit to the Deep Mani in 1951. The project involved meticulously analysing the couple's notebooks and photographs to glean details about where they had ventured, using least-cost analysis to model their potential routes and ground-truthing the results by walking and boating the routes ourselves. As in much of rural Greece, Mani's topography has changed substantially in the seven decades since the Leigh Fermors’ journey, with paved roads having replaced many of the Ottoman-era footpaths that locals once relied on for travel and transportation. While the transformed landscape we encountered prevented a complete re-enactment of the Leigh Fermors’ journey, it also offered an opportunity to embody key parts of their travelling experience. The results of our study are twofold: first, a detailed map of the route the Leigh Fermors followed based on our reading of their documentary sources; and second, an assessment of the utility of using least-cost analysis to model the routes of historical travellers.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.035
GPT teacher head0.230
Teacher spread0.195 · 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.

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

Citations1
Published2023
Admission routes2
Has abstractyes

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