Archives des mots-clés : computer science

Neither Computer Science, nor Information Studies, nor Humanities Enough: What Is the Status of a Digital Humanities Conference Paper?

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Authors : Laura Estill, Jennifer Guiliano

This paper explores the disciplinary and regional conventions that surround the status of conference papers throughout their lifecycle from submission/abstract, review, presentation, and in some cases, publication.

Focusing on national and international Digital Humanities conferences, while also acknowledging disciplinary conferences that inform Digital Humanities, this paper blends close readings of conference calls for papers with analysis of conference practices to reckon with what constitutes a conference submission and its status in relationship to disciplinary conventions, peer review, and publication outcomes.

Ultimately, we argue that the best practice for Digital Humanities conferences is to be clear on the review and publication process so that participants can gauge how to accurately reflect their contributions.

URL : Neither Computer Science, nor Information Studies, nor Humanities Enough: What Is the Status of a Digital Humanities Conference Paper?

DOI : https://doi.org/10.16995/dscn.8090

Gender disparity in publication records: a qualitative study of women researchers in computing and engineering

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Authors : Mohammad Hosseini, Shiva Sharifzad

Background

The current paper follows up on the results of an exploratory quantitative analysis that compared the publication and citation records of men and women researchers affiliated with the Faculty of Computing and Engineering at Dublin City University (DCU) in Ireland.

Quantitative analysis of publications between 2013 and 2018 showed that women researchers had fewer publications, received fewer citations per person, and participated less often in international collaborations.

Given the significance of publications for pursuing an academic career, we used qualitative methods to understand these differences and explore factors that, according to women researchers, have contributed to this disparity.

Methods

Sixteen women researchers from DCU’s Faculty of Computing and Engineering were interviewed using a semi-structured questionnaire. Once interviews were transcribed and anonymised, they were coded by both authors in two rounds using an inductive approach.

Results

Interviewed women believed that their opportunities for research engagement and research funding, collaborations, publications and promotions are negatively impacted by gender roles, implicit gender biases, their own high professional standards, family responsibilities, nationality and negative perceptions of their expertise and accomplishments.

Conclusions

Our study has found that women in DCU’s Faculty of Computing and Engineering face challenges that, according to those interviewed, negatively affect their engagement in various research activities, and, therefore, have contributed to their lower publication record.

We suggest that while affirmative programmes aiming to correct disparities are necessary, they are more likely to  improve organisational culture if they are implemented in parallel with bottom-up initiatives that engage all parties, including men researchers and non-academic partners, to inform and sensitise them about the significance of gender equity.

URL : Gender disparity in publication records: a qualitative study of women researchers in computing and engineering

DOI : https://doi.org/10.1186/s41073-021-00117-3

Does double-blind peer review reduce bias? Evidence from a top computer science conference

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Authors : Mengyi Sun, Jainabou Barry Danfa, Misha Teplitskiy

Peer review is essential for advancing scientific research, but there are long-standing concerns that authors’ prestige or other characteristics can bias reviewers. Double-blind peer review has been proposed as a way to reduce reviewer bias, but the evidence for its effectiveness is limited and mixed.

Here, we examine the effects of double-blind peer review by analyzing the review files of 5,027 papers submitted to a top computer science conference that changed its reviewing format from single- to double-blind in 2018.

First, we find that the scores given to the most prestigious authors significantly decreased after switching to double-blind review. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance significantly.

Second, the inter-reviewer disagreement increased significantly in the double-blind format.

Third, papers rejected in the single-blind format are cited more than those rejected under double-blind, suggesting that double-blind review better excludes poorer quality papers.

Lastly, an apparently unrelated change in the rating scale from 10 to 4 points likely reduced prestige bias significantly such that papers’ acceptance was affected.

These results support the effectiveness of double-blind review in reducing biases, while opening new research directions on the impact of peer-review formats.

URL : Does double-blind peer review reduce bias? Evidence from a top computer science conference

DOI : https://doi.org/10.1002/asi.24582

Prevalence of nonsensical algorithmically generated papers in the scientific literature

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Authors : Guillaume Cabanac, Cyril Labbé

In 2014 leading publishers withdrew more than 120 nonsensical publications automatically generated with the SCIgen program. Casual observations suggested that similar problematic papers are still published and sold, without follow-up retractions.

No systematic screening has been performed and the prevalence of such nonsensical publications in the scientific literature is unknown. Our contribution is 2-fold.

First, we designed a detector that combs the scientific literature for grammar-based computer-generated papers. Applied to SCIgen, it has a 83.6% precision. Second, we performed a scientometric study of the 243 detected SCIgen-papers from 19 publishers.

We estimate the prevalence of SCIgen-papers to be 75 per million papers in Information and Computing Sciences. Only 19% of the 243 problematic papers were dealt with: formal retraction (12) or silent removal (34).

Publishers still serve and sometimes sell the remaining 197 papers without any caveat. We found evidence of citation manipulation via edited SCIgen bibliographies. This work reveals metric gaming up to the point of absurdity: fraudsters publish nonsensical algorithmically generated papers featuring genuine references.

It stresses the need to screen papers for nonsense before peer-review and chase citation manipulation in published papers. Overall, this is yet another illustration of the harmful effects of the pressure to publish or perish.

URL : Prevalence of nonsensical algorithmically generated papers in the scientific literature

DOI : https://doi.org/10.1002/asi.24495

Science-Software Linkage: The Challenges of Traceability between Scientific Knowledge and Software Artifacts

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Authors : Hideaki Hata, Jin L.C. Guo, Raula Gaikovina Kula, Christoph Treude

Although computer science papers are often accompanied by software artifacts, connecting research papers to their software artifacts and vice versa is not always trivial. First of all, there is a lack of well-accepted standards for how such links should be provided.

Furthermore, the provided links, if any, often become outdated: they are affected by link rot when pre-prints are removed, when repositories are migrated, or when papers and repositories evolve independently.

In this paper, we summarize the state of the practice of linking research papers and associated source code, highlighting the recent efforts towards creating and maintaining such links.

We also report on the results of several empirical studies focusing on the relationship between scientific papers and associated software artifacts, and we outline challenges related to traceability and opportunities for overcoming these challenges.

URL : https://arxiv.org/abs/2104.05891

Assessment of gender divide in scientific communities

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Authors : Antonio De Nicola, Gregorio D’Agostino

Increasing evidence of women’s under-representation in some scientific disciplines is prompting researchers to expand our understanding of this social phenomenon. Moreover, any countermeasures proposed to eliminate this under-representation should be tailored to the actual reasons for this different participation.

Here, we take a multi-dimensional approach to assessing gender differences in science by representing scientific communities as social networks, and using data analytics, complexity science methods, and semantic methods to measure gender differences in the context, the attitude and the success of scientists.

We apply this approach to four scientific communities in the two fields of computer science and information systems using the network of authors at four different conferences. For each discipline, one conference is based in Italy and attracts mostly Italians, while one conference is international in both location and participants.

The present paper provides evidence against common narratives that women’s under-representation is due to women’s limited skills and/or less social centrality.

URL : Assessment of gender divide in scientific communities

DOI : https://doi.org/10.1007/s11192-021-03885-3

The role of metadata in reproducible computational research

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Authors : Jeremy Leipzig, Daniel Nüst, Charles Tapley Hoyt, Stian Soiland-Reyes, Karthik Ram, Jane Greenberg

Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results.

In addition to its role in research integrity, RCR has the capacity to significantly accelerate evaluation and reuse. This potential and wide-support for the FAIR principles have motivated interest in metadata standards supporting RCR.

Metadata provides context and provenance to raw data and methods and is essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described the relationship between metadata and RCR.

This article employs a functional content analysis to identify metadata standards that support RCR functions across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications.

Our article provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

URL : The role of metadata in reproducible computational research

Original location : https://arxiv.org/abs/2006.08589