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Record W2517762123 · doi:10.7202/1036372ar

Écrire sur Facebook, ou les sentiers de la reconnaissance

2016· article· fr· W2517762123 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnthropologie et Sociétés · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicPolitical and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

En Tunisie, l’ordre politico-graphique est clair : la langue arabe, seule langue reconnue dans la Constitution, s’exprime par l’alphabet arabe, le français par l’alphabet latin, les chiffres servent à exprimer des grandeurs et le tunisien n’a pas de visibilité officielle à l’écrit. Les écritures des Statuts sur Facebook, en revanche, défient ces arrangements. Les limites de ces usages y sont lâches, les graphies emmêlées, les arrangements révisés et le tunisien écrit apparaît, se répand et se normalise. Je propose de comprendre ces écritures comme des expressions d’une citoyenneté horizontale engageant un processus de reconnaissance d’une langue qui n’a pas de visibilité officielle à l’écrit. Facebook devient ainsi un espace de remise en question du rôle de l’État dans sa définition d’une forme scripturaire de citoyenneté. Je soutiens, enfin, que les processus de reconnaissance ne sont pas nécessairement étayés par des pratiques de luttes et de revendications mais qu’ils peuvent se dérouler de manière relativement banale et informelle.

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.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.017
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0250.001

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 head0.301
GPT teacher head0.585
Teacher spread0.284 · 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