Bibliographic record
Abstract
This essay explores the role of online communities and cyber-activism in fostering ‘real-life’ participation. It begins by revisiting a topic of controversy among citizenship studies scholars: the erosion vs. the expansion of citizenship and active participation instances in the past three decades. Arguably, participation in online communities is one of the most notable instances of reinventing active citizenship. While sceptics view the Internet and social capital as a contradiction in terms and deplore the waning of traditional communities, supporters of online participation emphasize the potential of the Internet to bring together people who would have otherwise never met in support of a cause. The second half of the essay will demonstrate that, in some cases, the actions of protest undertaken by online communities turn into “acts of citizenship” by challenging habitus, power and regulations (Isin, 2008). It will discuss the activity of Houses that Cry, a project created by architecture students in Bucharest in order to protect the architectural patrimony of the city. Their initiative can be considered an act of citizenship for two reasons: first, they shifted online protests from blogs and forums to the street, community and the media. Second, they transformed the protection of endangered historical buildings into a matter of public interest.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".