Search results

This study aims to demonstrate what myths of and about science are reproduced in this popular cultural work (movie – “Oppenheimer”). This is done by examining the unconscious hegemonic positions supported by the reproduction of stereotypical and mythical images of science.

Content/Text Analysis: The conceptual analysis of a cultural text – a film (“Oppenheimer”) – through a theoretical apparatus (B. Latour’s theory).

The film demonstrates its reproduction of three distinct elements. Firstly, it exhibits classic scientistic clichés pertaining to technoscience. Secondly, it highlights the replication of the individualized monomyth about the (super) hero, leading to the exclusion of the intricate conditions of technoscience’s existence. Lastly, the film aligns with the Californian ideology, as proposed by Barbrook.

The value of the text is twofold: (1) To show that the classical approaches of Bruno Latour are still relevant. (2) To show what hidden premises and myths about technoscience are being propagated through a work of pop culture (the film “Oppenheimer”) and, in effect, to show what kind of influence of cultural hegemony is at work here.

This study aims to address a systematic literature review (SLR) using bibliometrics on the relationship between academic integrity and artificial intelligence (AI), to bridge the scattering of literature on this topic, given the challenge and opportunity for the educational and academic community.

This review highlights the enormous social influence of COVID-19 by mapping the extensive yet distinct and fragmented literature in AI and academic integrity fields. Based on 163 publications from the Web of Science, this paper offers a framework summarising the balance between AI and academic integrity.

With the rapid advancement of technology, AI tools have exponentially developed that threaten to destroy students' academic integrity in higher education. Despite this significant interest, there is a dearth of academic literature on how AI can help in academic integrity. Therefore, this paper distinguishes two significant thematical patterns: academic integrity and negative predictors of academic integrity.

This study also presents several contributions by showing that tools associated with AI can act as detectors of students who plagiarise. That is, they can be useful in identifying students with fraudulent behaviour. Therefore, it will require a combined effort of public, private academic and educational institutions and the society with affordable policies.

This study proposes a new, innovative framework summarising the balance between AI and academic integrity.

By examining types of literacies taught by public libraries and the modes through which these programs were offered, this study aims to explore how public libraries might integrate data literacy training for the general public into existing library educational programs.

This study examined programs offered in 30 US public libraries during 2019 and 2020 to better understand types of literacy education announced to the public through library website listings and Facebook Events pages.

While public libraries offered educational programs in literacy areas ranging from basic reading and writing to technology, vocational skills, health literacy and more, data literacy training was not widely offered. However, this study identified many already-existing programs highly compatible for integrating with data literacy training.

This study offered new insights into both the literacies taught in public library programs as well as ways for public libraries to integrate data literacy training into existing educational programming, in order to better provide data literacy education for the general public.

This study aims to explore the implementation of artificial intelligence (AI) in archival practice by presenting the thoughts and opinions of working archival practitioners. It contributes to the extant literature with a fresh perspective, expanding the discussion on AI adoption by investigating how it influences the perceptions of digital archival expertise.

In this study a two-phase data collection consisting of four online focus groups was held to gather the opinions of international archives and digital preservation professionals (n = 16), that participated on a volunteer basis. The qualitative analysis of the transcripts was performed using template analysis, a style of thematic analysis.

Four main themes were identified: fitting AI into day to day practice; the responsible use of (AI) technology; managing expectations (about AI adoption) and bias associated with the use of AI. The analysis suggests that AI adoption combined with hindsight about digitisation as a disruptive technology might provide archival practitioners with a framework for re-defining, advocating and outlining digital archival expertise.

The volunteer basis of this study meant that the sample was not representative or generalisable.

Although the results of this research are not generalisable, they shed light on the challenges prospected by the implementation of AI in the archives and for the digital curation professionals dealing with this change. The evolution of the characterisation of digital archival expertise is a topic reserved for future research.

Exploiting a novel measure of hostile takeover exposure primarily based on the staggered adoption of state legislations, we explore a crucial, albeit largely overlooked, aspect of corporate social responsibility (CSR). In particular, we investigate CSR inequality, which is the inequality across different CSR categories. Higher inequality suggests a less balanced, more lopsided, CSR policy.

In addition to the standard regression analysis, we perform several robustness checks including propensity score matching, entropy balancing and an instrumental-variable analysis.

Our results show that more takeover exposure exacerbates CSR inequality. Specifically, a rise in takeover vulnerability by one standard deviation results in an increase in CSR inequality by 4.53–5.40%. The findings support the managerial myopia hypothesis, where myopic managers promote some CSR activities that are useful to them in the short run more than others, leading to higher CSR inequality.

Our study is the first to exploit a unique measure of takeover vulnerability to investigate the impact of takeover threats on CSR inequality, which is an important aspect of CSR that is largely overlooked in the literature. We aptly fill this void in the literature.

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.