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Record W1910018001

L’analyse des évaluations : une méthode originale au service d’une meilleure connaissance des forums en ligne et de leurs visiteurs invisibles, les lurkers.

2013· article· fr· W1910018001 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCommposite · 2013
Typearticle
Languagefr
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHumanitiesLignePolitical scienceSociologyArt
DOInot available

Abstract

fetched live from OpenAlex

Cet article presente les benefices qu’une recherche sur les forums peut tirer d’une methodologie mixte utilisant des methodes d’analyse variees : apporter des elements nouveaux, elaborer des concepts plus complexes et nuancer les resultats issus d’autres methodes d’analyse. L’article se penche particulierement sur une methode peu courante : l’analyse des evaluations des messages. Cette methode ne se limite pas a affiner les resultats obtenus par d’autres, elle offre aussi des enseignements inattendus. Premierement, elle met en evidence comment les contributeurs de ce forum utilisent les evaluations pour simultanement respecter les normes de contribution textuelle tout en les contournant. Ensuite, elle rend concrete la presence des visiteurs invisibles que la litterature appelle lurkers et permet d’en distinguer quatre profils differents. Enfin, cette methode permet d’interroger la pertinence du concept de communaute virtuelle dans le cadre des forums. This article discusses the benefits of using a mixed methodology with various analytical methods for online forums research. These methods include: bringing new elements, producing more complex concepts and qualifying the results from other methods of analysis. Here we focus on an unusual method: analysis of messages evaluations. This approach not only refines the results of other methods, but also offers unexpected contributions. First, it highlights how the forum contributors use evaluations in order to simultaneously observe the norms of textual contributions while still avoiding them. Moreover, this analysis makes concrete the presence of invisible visitors, lurkers, and distinguishes four different lurkers profiles. Finally, this method allows to question the relevance of the concept of virtual community related to forums surroundings.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0030.007
Open science0.0010.001
Research integrity0.0000.001
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 head0.387
GPT teacher head0.375
Teacher spread0.013 · 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