Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the effect of internet filtering, and its impact on marginalized groups including non‐governmental organizations, female activists, ethnic, and religious minorities, the younger generation and the increase of the digital divide in Iran. Design/methodology/approach The paper raises two main questions: to what extent do information and communications technologies (ICTs) and in particular, the internet, promote freedom of speech, and gender equality in Iran? What is the impact of state censorship and ICT filtering on these activities? To answer these research questions, the author uses narratives of the internet's usage along with a comparison study with other Middle Eastern countries to analyze the impact of ICTs on citizen's freedom of expression. Findings The paper argues that restrictions imposed on ICT tools and services by the Government of Iran which has been claimed to protect country's national security against the corruption and immorality imposed by Western countries not only affect the expansion of ICTs negatively but also civil liberties – thus increasing the digital divide internally, regionally, as well as on a global scale. Research limitations/implications Albeit this research is limited to the case study of Iran, the author believes that lessens learned from the Iran's case study can be applied to other Islamic countries and in particular countries located in the Middle East region. Practical implications ICT tools and services such as the internet and short message service are effective emancipatory media for citizens' participation and mobilization in democratic processes. Originality/value This paper contributes to the existing knowledge and understanding of the impact of ICTs on freedom and democracy.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".