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Gouri Ginde

Gouri Ginde appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Artificial IntelligenceDigital LibrariesHuman-Computer InteractionSoftware Engineering

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Published work

3 published item(s)

preprint2026arXiv

What Software Engineering Looks Like to AI Agents? -- An Empirical Study of AI-Only Technical Discourse on MoltBook

AI agents are increasingly framed as software-engineering teammates, yet most research studies them inside human-centered workflows. Little is known about the software-engineering discourse autonomous AI agents produce when they interact primarily with one another. This paper examines what autonomous AI agents discuss in MoltBook, an AI-agents-only social network, how that discourse is organized, and how it differs from human developer discourse. We combine human open coding of a 500-post sample, a concentration-plus-check topic-analysis pipeline over 4,707 English-filtered MoltBook technology posts, and a matched-instrument comparison against 5,211 GitHub Discussions posts. MoltBook technology discourse spans 12 recurring themes and is led by Security and Trust (27.4%). At the community level, activity is highly concentrated: the largest submolt contains 63.5% of posts and the Gini coefficient is 0.88, yet a stability-aware BERTopic pipeline still yields 32 non-outlier sub-topics. Compared with the GitHub Discussions baseline, MoltBook discourse contains fewer concrete, context-rich cues such as code-formatted artifacts, environment details, runtime failures, and reproduction steps; social mimicry appears only in a limited way, while idealization is mainly reflected through lower hedging. Overall, AI-only technical discourse is coherent but selective. It repeatedly returns to concerns such as security and trust, memory and context management, tooling and APIs, debugging and error handling, workflow automation, and infrastructure/ops, while omitting much of the concrete runtime and project-local detail common in human developer discourse. This may be because MoltBook contains fewer environment-specific failures, reproduction steps, and other concrete grounding cues.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

ScientoBASE: A Framework and Model for Computing Scholastic Indicators of non-local influence of Journals via Native Data Acquisition algorithms

Defining and measuring internationality as a function of influence diffusion of scientific journals is an open problem. There exists no metric to rank journals based on the extent or scale of internationality. Measuring internationality is qualitative, vague, open to interpretation and is limited by vested interests. With the tremendous increase in the number of journals in various fields and the unflinching desire of academics across the globe to publish in "international" journals, it has become an absolute necessity to evaluate, rank and categorize journals based on internationality. Authors, in the current work have defined internationality as a measure of influence that transcends across geographic boundaries. There are concerns raised by the authors about unethical practices reflected in the process of journal publication whereby scholarly influence of a select few are artificially boosted, primarily by resorting to editorial maneuvres. To counter the impact of such tactics, authors have come up with a new method that defines and measures internationality by eliminating such local effects when computing the influence of journals. A new metric, Non-Local Influence Quotient(NLIQ) is proposed as one such parameter for internationality computation along with another novel metric, Other-Citation Quotient as the complement of the ratio of self-citation and total citation. In addition, SNIP and International Collaboration Ratio are used as two other parameters.

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preprint2016arXiv

Visualisation of massive data from scholarly Article and Journal Database A Novel Scheme

Scholarly articles publishing and getting cited has become a way of life for academicians. These scholarly publications shape up the career growth of not only the authors but also of the country, continent and the technological domains. Author affiliations, country and other information of an author coupled with data analytics can provide useful and insightful results. However, massive and complete data is required to perform this research. Google scholar which is a comprehensive and free repository of scholarly articles has been used as a data source for this purpose. Data scraped from Google scholar when stored as a graph and visualized in the form of nodes and relationships, can offer discerning and concealed information. Such as, evident domain shift of an author, various research domains spread for an author, prediction of emerging domain and sub domains, detection of journal and author level citation cartel behaviors etc. The data from graph database is also used in computation of scholastic indicators for the journals. Eventually, econometric model, named Cobb Douglas model is used to compute the journals Modeling "Internationality" Index based on these scholastic indicators.

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