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Record W2802210206 · doi:10.3390/min8050183

Catalan Imitations of the Ligurian Taches Noires Ware in Barcelona (18th–19th Century): An Example of Technical Knowledge Transfer

2018· article· en· W2802210206 on OpenAlexaboutno aff
Roberta Di Febo, Lluı̀s Casas, Claudio Capelli, Roberto Cabella, Oriol Vallcorba

Bibliographic record

VenueMinerals · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsnot available
FundersUniversitat de BarcelonaMinisterio de Ciencia e InnovaciónHenan Institute of Science and TechnologyMinisterio de Economía y Competitividad
KeywordsCatalanPotteryMediterranean climateHomogeneousDiversification (marketing strategy)GeographyGermanArchaeologyCorsicanHumanitiesArtBusiness

Abstract

fetched live from OpenAlex

The ware called Taches Noires was developed in Albisola (Liguria, NW Italy) during the 18th century. In just a few years, it spread all over the Mediterranean (Italy, France, Spain, Tunisia, and Greece) and also in the New World (Canada, the Caribbean Islands, and Mexico). The success of the Taches Noires ware was so massive that it was soon copied by Spanish and French workshops. A collection of Catalan imitations and Ligurian imports found in Barcelona were analysed and compared to previously existing data from Barcelona productions, as well as reference samples from Albisola. The study proved the presence of both local imitations and original Albisola imports. The analysis showed a homogeneous product of high technical quality for the Albisola pottery. On the contrary, the local imitations presented a greater diversification in the choice and manipulation of the raw materials, probably related to the existence of different workshops engaged in the manufacturing of these products. Nevertheless, for one of the local groups, ceramists adopted a glaze recipe similar to the one used in Albisola, clearly indicating a direct transfer of knowledge, and possibly of potters, from Albisola to Barcelona.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.077
GPT teacher head0.264
Teacher spread0.187 · 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 designBench or experimental
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

Citations7
Published2018
Admission routes1
Has abstractyes

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