Between the hammer and the Anvil: blogs, bloggers, and the public sphere in Egypt
Bibliographic record
Abstract
This dissertation uses Habermas's concept of the public sphere to explore the impact of alternative and citizen media, especially blogs, in Egypt. It examines the dynamic relationship between politics and media in Egypt to better understand the role of new media and blogs in this process. In interviews conducted in Egypt, I asked bloggers, human rights activists, and journalists about their motivations for blogging, their identities as bloggers and activists, the nature of the relationship between blogging and traditional media practitioners, and the challenges that hinder the efficacy of blogging as a social and political force. These interviews revealed that Internet access is no longer a barrier to the political use of the Internet. Instead, the responses of my participants suggest that the existing challenges include cultural norms (for example, gender bias), economic factors (for example, poverty), and the widespread perception that the Internet is not a legitimate means for political change. Aside from the common motivation for blogging—self-expression—blogs in Egypt are used to criticize the political order and mobilize public opinion against the regime. An in-depth case study and textual analysis of one of Egypt's and the Middle East's most popular political blogs—Al-Wa'i al-Masry—shows that political blogging in Egypt is a main form of political activism. My interviews with bloggers, human rights activists, and journalists, as well as the case-study and textual analysis of Al-Wa'i al-Masry show that blogging in Egypt has succeeded in breaking down political and social taboos in Egypt (often ignored by the traditional media) and has played an important role in the current debate about political reform in Egypt. However, despite these successes, one of the major conclusions of this dissertation is that the very language used in blogs undermines the possibility of achieving the rational-critical discourse necessary to meet one of the most fundamental conditions of the Habermasian public sphere, and further that this language raises doubts about the civility and ethics of blogging in general.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".