MétaCan
Menu
Back to cohort
Record W4382794699 · doi:10.7592/ejhr.2023.11.2.773

What did the Portuguese laugh at 200 years ago?

2023· article· en· W4382794699 on OpenAlexfundno aff
João Pedro Rosa Ferreira

Bibliographic record

VenueEuropean Journal of Humour Research · 2023
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaFederation for the Humanities and Social Sciences
KeywordsLaughterAppropriationPoliticsPortugueseMeaning (existential)ComicsHistoryLiteratureSociologyAestheticsMedia studiesArtPolitical scienceLinguisticsLawPhilosophyEpistemology

Abstract

fetched live from OpenAlex

This article aims to identify the existence of a laughter community in Portugal in the late eighteenth and early nineteenth centuries. Based on research into the beginnings of humour in periodicals published in Portugal, a corpus consisting of newspapers published between 1797 and 1835 was analysed, from the first in which humour was used systematically as a resource (Almocreve de Petas) until the establishment of the Constitutional Monarchy. With the concept of laughter community in mind, evidence was sought that it was present in the period that covers the political, social and economic transition from the Ancien Régime to modern society, having as main players writers, editors, printers, readers and listeners, in a process of production, reception, circulation and appropriation of ideas and meanings. This process, which developed in the public sphere, also played a part in forming incipient public opinion. To detect evidence of this community, clichés, jocular expressions and comic stories conveyed by the periodicals were identified. Very often they were found to have kept the same meaning they had at the time, while some expressions have survived with slight changes, and others simply no longer make people laugh.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.013

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 head0.205
GPT teacher head0.462
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Humour ResearchSame topicHumor Studies and ApplicationsFrench-language works237,207