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A randomized trial of a lab-embedded discourse intervention to improve research ethics.
We report a randomized trial of a research ethics training intervention designed to enhance ethics communication in university science and engineering laboratories, focusing specifically on authorship and data management. The intervention is a project-based research ethics curriculum that was designed to enhance the ability of science and engineering research laboratory members to engage in reason giving and interpersonal communication necessary for ethical practice. The randomized trial was fielded in active faculty-led laboratories at two US research-intensive institutions. Here, we show that laboratory members perceived improvements in the quality of discourse on research ethics within their laboratories and enhanced awareness of the relevance and reasons for that discourse for their work as measured by a survey administered over 4 mo after the intervention. This training represents a paradigm shift compared with more typical module-based or classroom ethics instruction that is divorced from the everyday workflow and practices within laboratories and is designed to cultivate a campus culture of ethical science and engineering research in the very work settings where laboratory members interact
Drawing Elena Ferrante's Profile. Workshop Proceedings, Padova, 7 September 2017
Elena Ferrante is an internationally acclaimed Italian novelist whose real identity has been kept secret by E/O publishing house for more than 25 years. Owing to her popularity, major Italian and foreign newspapers have long tried to discover her real identity. However, only a few attempts have been made to foster a scientific debate on her work.
In 2016, Arjuna Tuzzi and Michele Cortelazzo led an Italian research team that conducted a preliminary study and collected a well-founded, large corpus of Italian novels comprising 150 works published in the last 30 years by 40 different authors. Moreover, they shared their data with a select group of international experts on authorship attribution, profiling, and analysis of textual data: Maciej Eder and Jan Rybicki (Poland), Patrick Juola (United States), Vittorio Loreto and his research team, Margherita Lalli and Francesca Tria (Italy), George Mikros (Greece), Pierre Ratinaud (France), and Jacques Savoy (Switzerland).
The chapters of this volume report the results of this endeavour that were first presented during the international workshop Drawing Elena Ferrante's Profile in Padua on 7 September 2017 as part of the 3rd IQLA-GIAT Summer School in Quantitative Analysis of Textual Data. The fascinating research findings suggest that Elena Ferrante\u2019s work definitely deserves \u201cmany hands\u201d as well as an extensive effort to understand her distinct writing style and the reasons for her worldwide success
Mining online diaries for blogger identification
In this paper, we present an investigation of authorship
identification on personal blogs or diaries, which are different from other types of text such as essays, emails, or articles based on the text properties. The investigation utilizes couple of intuitive feature sets and studies various parameters that affect the identification performance.
Many studies manipulated the problem of authorship
identification in manually collected corpora, but only few
utilized real data from existing blogs. The complexity of
the language model in personal blogs is motivating to
identify the correspondent author. The main contribution
of this work is at least three folds. Firstly, we utilize the LIWC and MRC feature sets together, which have been
developed with Psychology background, for the first time
for authorship identification on personal blogs. Secondly, we analyze the effect of various parameters, and feature sets, on the identification performance. This includes the number of authors in the data corpus, the post size or the word count, and the number of posts for each author.
Finally, we study applying authorship identification over a limited set of users that have a common personality attributes. This analysis is motivated by the lack of standard or solid recommendations in literature for such task, especially in the domain of personal blogs.
The results and evaluation show that the utilized features
are compact while their performance is highly comparable
with other larger feature sets. The analysis also confirmed
the most effective parameters, their ranges in the data
corpus, and the usefulness of the common users classifier
in improving the performance, for the author identification
task
Text authorship identified using the dynamics of word co-occurrence networks
The identification of authorship in disputed documents still requires human
expertise, which is now unfeasible for many tasks owing to the large volumes of
text and authors in practical applications. In this study, we introduce a
methodology based on the dynamics of word co-occurrence networks representing
written texts to classify a corpus of 80 texts by 8 authors. The texts were
divided into sections with equal number of linguistic tokens, from which time
series were created for 12 topological metrics. The series were proven to be
stationary (p-value>0.05), which permits to use distribution moments as
learning attributes. With an optimized supervised learning procedure using a
Radial Basis Function Network, 68 out of 80 texts were correctly classified,
i.e. a remarkable 85% author matching success rate. Therefore, fluctuations in
purely dynamic network metrics were found to characterize authorship, thus
opening the way for the description of texts in terms of small evolving
networks. Moreover, the approach introduced allows for comparison of texts with
diverse characteristics in a simple, fast fashion
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