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Record W3044598005 · doi:10.12797/saac.19.2015.19.05

Mapping Mistakes: The Cartographic Confusion of Ancient Kleitor

2015· article· en· W3044598005 on OpenAlexaff
Matthew Maher

Bibliographic record

VenueStudies in Ancient Art and Civilization · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeological Research and Protection
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsRepresentation (politics)Plan (archaeology)ConfusionExcavationSchematicScholarshipHistoryArchaeologyPoliticsPsychologyEngineering

Abstract

fetched live from OpenAlex

The ancient Greek city of Kleitor lies in a small valley in north central Arkadia. Although only recently the target of systematic excavations, the first plan of its remains was published almost 200 years ago. While this earliest plan is essentially correct in the details, it is also a simple schematic representation with little topographical detail. When a revised plan of the site – comprising a much more skillful representation of the topography – was published in the late 19th century, it soon supplanted the original in the scholarship. Hidden behind its topographic accuracy and artistic flourishes, however, lies the fact that the mapping of the archaeological remains themselves was incorrect. Consequently, as this plan continued to be modified and reproduced throughout the following century, so too were its mistakes duplicated and exaggerated. Showing the cartographical evolution in the representation of ancient Kleitor and its reception by scholars, this paper demonstrates how scholars have constructed their interpretations of the remains around the unintentional predisposition to equate artistic quality with accuracy, and the consequences of this bias on the archaeological interpretations of the site.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0080.019
Scholarly communication0.0090.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.116
GPT teacher head0.305
Teacher spread0.189 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

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