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Record W3015901028 · doi:10.1075/rs.19012.ehr

Are online news comments like face-to-face conversation?

2020· article· en· W3015901028 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRegister Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsConversationArgumentativeOnline discussionFace (sociological concept)Face-to-faceComputer scienceLinguisticsRegister (sociolinguistics)PsychologyCommunicationWorld Wide WebEpistemology

Abstract

fetched live from OpenAlex

Abstract This article focuses on the question of whether online news comments are like face-to-face conversation or not. It is a widespread view that online comments are like “dialogue”, with comments often being referred to as “conversations”. These assumptions, however, lack empirical back-up. In order to answer this question, we systematically explore register-relevant properties of online news comments using multi-dimensional analysis (MDA) techniques. Specifically, we apply MDA to establish what online comments are like by describing their linguistic features and comparing them to traditional registers (e.g. face-to-face conversation, academic writing). Thus, we tap the SFU Opinion and Comments Corpus and the Canadian component of the International Corpus of English . We show that online comments are not like spontaneous conversation but rather closer to opinion articles or exams, and clearly constitute a written register. Furthermore, they should be described as instances of argumentative evaluative 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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.726
Threshold uncertainty score0.789

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.152
GPT teacher head0.343
Teacher spread0.191 · 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