Bibliographic record
Abstract
Cyberfeminism has emerged as one of the key recent innovations in feminism that harnesses the power of online technologies to promote gender equality and social justice. The advent of the internet has also significantly impacted the feminist movement in the Arab region in recent years, where Arab women's cyberactivism has contributed a new chapter to the history of both Arab feminism and the region. In the context of the Arab Spring, particularly, digital media have given women activists an unprecedented visibility through their strategic roles at three important stages: before the outbreak of the Arab revolutions to express “publicly” their social and political grievances without the fear of retaliation, during the series of uprisings in the mobilization, documentation of the events, and cultural dissemination phases, and, finally, in the aftermath of the Arab revolutions where women activists continue advocating, through digital storytelling and art and activism, the idea of the ongoing gender revolution today. Therefore, such a new technologically enabled visibility actually defies the traditional and widespread dichotomy of men versus women and public versus private that is used to characterize Arab and Muslim women's lives; the online sphere, therefore, becomes a “gateway” through which Middle Eastern women can access the public space, make their voices heard, and advocate for their rights as equal citizens.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".