Bibliographic record
Abstract
Operating outside the framework of traditional systems of governance and civic engagement, the digitally-mediated, networked society referred to as the “Fifth Estate” presents the general public with a unique opportunity to reinvigorate the public watchdog role. While previous discussions of the Fifth Estate have emphasized that the communicative power it enables can help to hold government to account, specific strategies have yet to be clearly identified. This paper presents three strategies for activating a digitally-mediated Fifth Estate: 1) building an online community of networked individuals, 2) shaping pre-existing digital platforms to enable members of the public to contribute focused and pointed user-generated content, and 3) developing targeted content to be shared and distributed. These strategies are presented in the context of the successful media reform battle to defeat Canada’s Bill C-30, an attempt by the Canadian government to expand upon its cyber-surveillance capabilities. The Stop Online Spying Coalition is presented as an example of the first strategy; online petitions, digital form letters and the #TellVicEverything Twitter attack are among the examples of the second strategy; and Openmedia.ca’s Stop Online Spying web materials, various online videos and the Vikileaks Twitter attack are examples of the third strategy.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".