Meso News-Spaces and Beyond: News-Related Communication Occurring Between the Public and Private Domains
Bibliographic record
Abstract
the concept of meso news-spaces refers to online spaces located between the private and public realms, where everyday users, more professional media actors, or both, can produce and share news-related content among each other, yet not to a wide audience.Such spaces are afforded by digital media platforms, including, but not limited to, Facebook groups, X spaces, and group chats on Wechat, WhatsApp, or telegram.this special issue is devoted to further understanding news-related communication that occurs neither in fully public nor fully private realms, but between or across the two.In the introduction to the special issue, we demonstrate the significance of meso news-spaces by considering the example of the use of WhatsApp groups in the mobilization of the pro-democracy movement in Israel in 2023.We then consider the challenges that meso news-spaces pose for researchers, in terms of conceptualization, research ethics, and context.We conclude with a review of the articles of the special issue, and with directions for future research around this phenomenon, that is proving to be a significant one in the digital news environment.In January 2023, Israel's justice minister introduced part of a plan that sought to limit the authority of the judiciary and grant significantly more power to the executive, posing threats to Israeli democracy (roznai and cohen 2023).the subsequent months saw the uprising of a large-scale and persistent grassroots protest movement (Linder 2023).Bottom-up protests around the country were mobilized mainly through large-scale WhatsApp and telegram groups, run by hundreds of grassroots organizations.Some were "quiet" groups, serving only to inform about upcoming protests; others were active discussion groups, some with hundreds of messages a day (see Kligler-Vilenchik and tenenboim 2020), where people shared and discussed news about the planned "reform" and attempts to block it.there were also pre-existing groups-e.g., for workplaces, parents of schoolchildren, or neighborhoods-which suddenly became a hub for discussion around the news and mobilization of protest.countless Israelis became part of one, several, or many such groups.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".