Bibliographic record
Abstract
How do producers and designers of Internet services and contents conceive of their publics? Both media studies and Internet studies have highlighted how actual publics and audiences remain unpredictable, heterogeneous and often quite different from how they are imagined. Furthermore, traditional notions and theory-laden terms are frequently used by marketers, journalists and scholars to refer to Internet publics without specifying in what sense they are using them--e.g. communities, social networks, friends, fans, amateurs, customers. This roundtable focuses on potential discrepancies between Internet professionals’ representations of their publics (including their expectations and motivations) and what empirical studies reveal about them. It will also address the imaginaries mobilized in professionals’ representations of their users and their products’ usage. How are these imaginaries influencing design practices? To what extent do actual users correspond to targeted (imagined) publics? The initial speakers will provide some insights to these questions by drawing on empirical case studies that constitute a sample of a broader collective effort to be published in an upcoming special issue of the French-language journal Communication . These studies look at various imaginaries summoned by Internet devices such as "science 2.0", "brand community" or "political Web". Five speakers (including the two organizers) will ignite the discussion: Alexandre Coutant, co-chair (University of Quebec at Montreal, Canada); Guillaume Latzko-Toth, co-chair (Laval University, Canada); Florence Millerand (University of Quebec at Montreal, Canada); Sandrine Roginsky (Catholic University of Leuwen, Belgium) and Julia Velkovska (Orange Labs, France).
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.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.025 | 0.025 |
| Insufficient payload (model declined to judge) | 0.122 | 0.052 |
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".