MORE THAN MEETS THE EYES: THE LENS OF VISIBILITY IN INTERNET RESEARCH
Bibliographic record
Abstract
The objective of this panel is to examine the analytical and empirical relevance of the “visibility lens” for Internet research. In the past decade, researchers have started to take a specific interest in the constitutive role of online visibility in the organization of social reality. Studies have underlined the fundamental role of visibility afforded by digital technologies in the social recognition or exclusion of individuals, groups, and communities. They have also identified visibility and its management as being constitutive of social identities, relations, and practices among actors in a variety of fields. So far, Internet researchers have provided various definitions and operationalizations of online visibility. For example, visibility can be apprehended as both a political lever for individuals and collectives or as a conceptual category for researchers to make sense of social reality. Visibility is also frequently associated with digital materiality. As such, it is sometimes used as a criterion to categorize digital technologies regarding the control they allow for users to manage and disclose personal contents or activities. Furthermore, visibility can also be conceptualized as an affordance that is enabled by the functionalities of digital technologies and enacted through their situated uses. In this panel, presenters will raise theoretical, methodological, and ethical issues linked to visibility by drawing from a series of case studies. They will then draw similarities and contrasts between cases, as well as discuss the implications and, indeed, the relevance of formalizing the lens of visibility in the field of Internet research.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.051 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| 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".