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Cyberfeminism in the Arab World

2020· other· en· W3042143958 on OpenAlexaff
Maha Tazi

Bibliographic record

VenueThe International Encyclopedia of Gender, Media, and Communication · 2020
Typeother
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsConcordia University
Fundersnot available
KeywordsFeminismPolitical sciencePublic sphereContext (archaeology)Middle EastPoliticsSocial mediaGender studiesMedia studiesSocial movementSociologyLawHistory

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.651
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.303
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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