Ethics and social media: Implications for sociolinguistics in the networked public<sup>1</sup>
Bibliographic record
Abstract
As a popular agora for writing identity into being, the networked public of social media sites presents exciting and unprecedented possibilities for sociolinguistic research. At the same time, these sites raise a wealth of unfamiliar methodological and ethical issues, and debate concerning appropriate ethical measures for research targeting online discourse communities is emergent. One of the most pressing debates concerns the visibility of online interaction (i.e. its locus between the public and private ends of the continuum). Although they exist freely online, networked publics are not public forums. They are governed by both personal and communal norms, and they are networked. This combination of factors gives rise to unique ethical challenges, particularly in the case of Facebook, an accessible and data‐rich, yet problematic, research site. This paper reviews the ethical difficulties presented by Facebook, and presents a framework for ethnographic sociolinguistic research that uses this site as a source of data. En tant qu’espace public privilégié pour la création de l’identitéà travers l’écriture, le public ≪réseauté≫ (interconnecté) des sites des médias sociaux présente des possibilités prometteuses et sans précédent pour la recherché en sociolinguistique. Cependant, ces sites soulèvent de nombreuses questions méthodologiques et éthiques qui sont nouvelles, d’où l’émergence de discussions portant sur les mesures éthiques appropriées pour la recherche qui vise les communautés discursives en ligne. Un des débats concerne la visibilité de l’interaction en ligne (c.‐à‐d. sa place entre les extrémités privée et publique du continuum). Bien qu’ils existent de façon libre en ligne, les réseaux publics ne sont pas des forums publics. Ils sont gouvernés à la fois par des normes personnelles et communautaires, et ils sont interconnectés. Cette combinaison de facteurs donne naissance à des défis éthiques uniques, en particulier dans le cas de Facebook, un site de recherche accessible et riche en données, mais problématique. Cet article passe en revue les difficultés éthiques que Facebook présente, et offre un cadre de travail pour la recherche sociolinguistique ethnographique utilisant ce site comme source de données. [French]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.093 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".