Computer Science

Introduction functions as a showcase in research articles (RAs). It motivates the reader to read the rest of the paper. However, writing a well-crafted introduction is a complex task, mainly when the writer generates the manuscript in another language. This study investigated the rhetorical differences/similarities employed in the introductions of RAs published in Iranian and international ISI journals in Computer Sciences (CS) using Swales (2004) CARS model. Two sets of CS RAs (30 each) were randomly selected. Frequency and non-parametric tests were used to examine the differences between the two groups of introductions. The results indicated that M 1 S 1 (Generalizing the topic), M2 1A (Indicating the gap), M3 S1 (Describing the research), M3 S4 (Methods Summary), and M 3 S 6 (Stating research advantages) were used with high frequencies. M 2 S 2 (Announcing positive justification) was absent, and the others were in low preferences. Also, the Analysis illustrated a statistically significant variation between the introductions concerning the use of M3S7 (Demarcating the Research Organization). Findings support genre-based pedagogy in scientific writing classes to make the graduate CS students aware of these rhetorical structures conventional to introductions in CS RAs.

Retracted publications continue to influence scholarship long after withdrawal. This study assembles a curated set of 169 Iran-affiliated retractions in computer science, data science, and electrical engineering from 2008 to 2024, links them to citing and cited works through two complementary retrieval pipelines, and constructs an expanded citation network of 1'694 nodes and 1,703 edges. We quantify retraction reasons and timing, community structure, node centrality, self-citation patterns, author and institutional concentration, international co-authorship, and a field-adjusted national benchmark. Misconduct-related causes predominate. The average interval from publication to retraction increased into 2021 and has since begun to shorten. The citation network exhibits strong community structure with three major thematic clusters. Centrality profiling isolates five retracted works that function as hubs, often reinforced by self-citation loops. Contribution is highly concentrated among a small set of authors and institutions, while collaboration extends across multiple regions beyond Iran. A field-adjusted retraction rate places the national record among mid-tier producers. These results identify practical leverage points to reduce downstream spread of invalidated findings: persistent indexing flags on hub retractions, routine screening of citations to retracted work, and focused attention on repeat patterns in self-citation and institutional clusters. The study offers a reproducible dual-pipeline approach, a full centrality profile of an enlarged network, and actor-level diagnostics that support targeted integrity interventions.