MétaCan
Menu
Back to cohort
Record W1616662409 · doi:10.4000/eccs.216

Comment Travailler La Mémoire Sur Twitter

2014· article· fr· W1616662409 on OpenAlexaffabout
Alexandre Turgeon

Bibliographic record

VenueÉtudes canadiennes / Canadian Studies · 2014
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Tout au long de la grève générale étudiante et de la campagne électorale québécoise de 2012, un phénomène fascinant s’est produit sur Twitter que j’appelle la Grande Noirceur et Révolution tranquille 2.0. Du 16 mai au 12 septembre 2012, j’ai relevé 6 000 tweets qui évoquent le souvenir de la Grande Noirceur et de la Révolution tranquille. J’ai constaté à quel point Twitter est un médium privilégié pour étudier ces questions sensibles touchant à la mémoire collective, aux usages du passé et au rapport au passé. Limités à seulement 140 caractères, les utilisateurs doivent être synthétiques, d’où le recours à ces images signifiantes dans l’imaginaire collectif québécois. Revenant sur mon parcours, je chercherai à répondre à la question suivante : Comment travailler la mémoire sur Twitter?

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.731
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.221
GPT teacher head0.289
Teacher spread0.068 · 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 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

Citations1
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueÉtudes canadiennes / Canadian StudiesSame topicCultural Insights and Digital ImpactsFrench-language works237,207