E-democracy remixed: Learning from the BBC's Action Network and the shift from a static commons to a participatory multiplex
Bibliographic record
Abstract
This paper examines a five-year initiative by the UK's public service broadcaster, the BBC, to reinvigorate civic engagement at a time of declining public participation in politics. The Action Network project, originally called iCan, ran from 2003 to 2008 and was one of the most high profile and ambitious attempts by a public service broadcaster to foster eParticipation through an online civic commons. This study analyzes Action Network within the context of conceptualizations of the Internet as a networked, distributed and participatory environment and the shift towards what scholars describe as a networked public sphere. It suggests that the project did not have the impact anticipated as it was borne out of a paternalistic broadcast legacy, out of step with the trend towards distributed and collaborative discourse online that reassesses the notion that the public is simply a resource to be managed. This paper argues that the BBC experience provides lessons in how the media, and specifically public service broadcasters, can contribute towards greater political participation and democratic dialogue through the Internet by adopting Web 2.0 approaches that enable citizens to engage on different levels and at different times, depending on contexts.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".