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Record W4313336731 · doi:10.17651/onomast.66.10

Material traces of past cultures as a motive for the creation of Spanish place names

2022· article· en· W4313336731 on OpenAlexfundno aff
Stefan Ruhstaller, María Dolores Gordón Peral

Bibliographic record

VenueOnomastica · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicArchaeological and Historical Studies
Canadian institutionsnot available
FundersInstitute of Aboriginal Peoples Health
KeywordsToponymyPoint (geometry)ArchaeologyHistoryProcess (computing)LinguisticsComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

Place names referencing the material traces of past cultures are relatively common in the microtoponymy, of Spanish-speaking areas. Since they were created by rural speech communities completely lacking in historical and archaeological culture, they make it possible to reconstruct how realities of archaeological interest (fragments of tools and building materials, ruins of buildings, dolmens, menhirs, tombs, old coins, inscriptions, engraved or painted cave art, among others) were popularly perceived and interpreted long before becoming objects of scientific study. Taking an extensive toponymic corpus as its starting point, this paper presents an exhaustive classification of such names, differentiating those of a purely descriptive nature from those intended to provide answers to questions concerning the origins, age, and purpose of the enigmatic discoveries. This toponomastic analysis facilitates the rigorous study of the process of onymic creation and its underlying motives.

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)0.004
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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.013
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.231
Teacher spread0.208 · 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
Published2022
Admission routes1
Has abstractyes

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