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Record W6894074421 · doi:10.5281/zenodo.6364772

Catalan Translation and Francoism (1939-1975)

2022· book-chapter· en· W6894074421 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typebook-chapter
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCatalanCensorshipPoliticsDictatorshipGlobeIdeologyState (computer science)

Abstract

fetched live from OpenAlex

As a measure of political and editorial control, many totalitarian regimes have turned their eye to translation. Considered one of the most efficient tools for modernising cultures it has been either closely watched or directly prohibited across the globe and during all periods of history. This article looks at one of the most exceptional cases in contemporary Europe: Catalan translation under the Franco dictatorship (1936-1975). For more than two decades the regime carried out double censorship: ideological and linguistic. While books were not permitted for publication if they questioned the religion, morality or politics imposed in any of the languages of the State (Basque, Catalan, Galician or Spanish), until 1962 translations into any of the “other” languages that were not Castilian Spanish were particularly hounded. From 1962 onwards a change in the legislation, which had allowed the “minority” or “minoritised” languages to be penalised automatically, meant that translation into Catalan experienced a kind of revivification and, making up for lost time, the Catalan language became one of the biggest target languages in translation during the 1960s (as statistical studies have shown). What were the “official” criteria for censorship and which arguments were put forward? How were the prohibited works singled out? What kind of works were translated? Who were the authors? From which languages? Who was responsible for the translations? Which publishers were the first to dodge the police controls and go ahead with the sale of the books? And what was the reception like at the time, if indeed there was a reception? These are some of the many questions that immediately arise and ones we will try to answer. Because, despite everything, the constant attempts to silence the transmission of foreign voices by means of a persecuted language did not manage to achieve their final objective: the eradication of otherness and, ultimately, of the language.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.237
Threshold uncertainty score0.472

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0060.005
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.004

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 head0.089
GPT teacher head0.242
Teacher spread0.153 · 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 designNot applicable
Domainnot available
GenreOther

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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