Public Spheres in Private Spaces: How Capital Will Stop the Web’s Democratic Potential
Bibliographic record
Abstract
In the late 1990s and into the early part of the new millennium, the vast, open, seemingly free space of the Internet allowed for many communication and political science scholars to bask in the optimism of a new communication system that would allow for increased debate, deliberation, and flow of information (Kellner, 1998). Notable scholars like Castells (1996) and Benkler (2006) led the charge of conversation in regards to the network society, and the democratizing impacts that such communication technologies could potentially provide. More recently, Internet optimists, like Shirky (2008, 2011), have expressed the role that digital technologies, mostly in terms of the Internet, can have in allowing for widespread democratizing communication, social movements and political action in its ability to organize and mobilize individuals, both in online discussion spaces, and in the “real” world.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.028 | 0.037 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".