110 research outputs found
Film censorship in Franco's Spain: the transforming power of dubbing
Since the invention of cinema, the prominence and significance of the moving image have never been underestimated by the powers-that-be, especially, though not exclusively, in totalitarian states, where foreign films and their translations are, and have been, ideologically controlled in order to avoid any conflict with the socio-cultural values predicated by the rulers of the hosting community. This paper focuses on the dubbing into Spanish of the classical film The Barefoot Contessa (Joseph L. Mankiewicz, 1954), in which glamorous Ava Gardner plays the role of a voluptuous Spanish flamenco dancer that becomes an international film star in the USA. Hollywoodâs appropriation and subsequent representation and internationalisation of Spanish mores and customs, embodied in the film by Ava Gardner and her Spanish family, was diametrically at odds with the values and virtues advocated by the Francoist regime (1939-1975), making this film a battleground for ideological manipulation and forcing the unleashing of a creative remediation process aimed at shrouding any criticism of Spanish interests or customs and cementing traditional values cherished by the regime
Subtitlers on the Cloud: The Use of Professional Web-based Systems in Subtitling Practice and Training
The bourgeoning and rapid evolution of cloud-based
applications has triggered profound transformations
in the audiovisual translation (AVT) mediascape. By
drawing attention to the major changes that webbased ecosystems have introduced in localisation
workflows, we set out to outline ways in which these
new technological advances can be embedded in the
AVT classroom. Along these lines, the present study
sets out to explore the potential benefits of cloud
platforms in AVT training curricula by exploring ways
in which this technology can be exploited in subtitling
training. An analysis of current subtitling practices
and tools, localisation workflows, and in-demand
skills in the AVT industry will be followed by an
experience-based account on the use of cloud-based
platforms in subtitler training environments to
simulate and carry out a wide range of tasks. Our
study pivots around the idea that cloud subtitling
might prove useful to bridge the technological gap
between academic institutions and the profession as
well as to enhance the distance-learning provision of
practice-oriented training in subtitling
Introduction: latest advancements in audiovisual translation education
The consumption of audiovisual content, from the more traditional animations, documentaries, movies, and TV shows to the more recent online user-generated content found on social media platforms, including video games, has grown exponentially over the last few decades. The omnipresence of screens in society has led to transformations in audiencesâ watching habits, now impatient to enjoy their programmes as soon as possible and inclined to binge watch. Recent technological advances in the production of specialist audiovisual translation (AVT) software and web-based applications have paved the way for further changes and enhancements in the ways professionals localise audiovisual content and in the nature of the services provided. This special issue sheds light on the current teaching and learning practices, methodologies and issues encountered by translator trainers specialised in AVT, with particular emphasis on pedagogical innovation, media accessibility, and translation technology
Isotopic evidence for mobility in the Copper and Bronze Age Cemetery of Humanejos (Parla, Madrid): a diachronic approach using biological and archaeological variables
Over the last several decades, the application of aDNA and strontium isotope analyses
on archaeologically recovered human remains has provided new avenues for the
investigation of mobility in past societies. Data on human mobility can be valuable
in the reconstruction of prehistoric residential patterns and kinship systems, which
are at the center of human social organization and vary across time and space. In
this paper, we aim to contribute to our understanding of mobility, residence, and
kinship patterns in late Prehistoric Iberia (c. 3300â1400BC) by providing new strontium
data on 44 individuals from the site of Humanejos (Parla, Madrid). The study
presented here is multi-proxy and looks at these new data by interweaving biological,
chronological, and archaeological information. This analysis found that 7/44
individuals buried at Humanejos could be identified as non-local to the necropolis.
Although more men (n = 5) than women (n = 2) were found in the non-local category,
and more non-local individuals were identified in the pre-Bell Beaker (n = 5)
than in Bell Beaker (n = 1) or Bronze Age (n = 1), we find no statistically significant
differences concerning sex or time period. This contrasts with other archaeological
datasets for late prehistoric Europe which suggest higher female mobility, female
exogamy, and male-centered residential patterns were common. At Humanejos, we
have also identified one non-local female whose exceptional Beaker grave goods
suggest she was an individual of special status, leading to additional questions about
the relationships between gender, mobility, and social position in this region and
time periodThe project leading to this
publication has received funding from the European Unionâs Horizon 2020 research and innovation program
under the Marie Sklodowska-Curie Grant Agreement No 891776, project âWOMAM. Women,
Men and Mobility: Understanding Gender Inequality in Prehistory.â This article was also supported by
the Spanish Ministerio de Ciencia e InnovaciĂłn Grants No. PID2019-105690 GB-I00 and HAR2013-
47776-R, the DirecciĂłn General de Patrimonio Cultural (Comunidad de Madrid) and the SFB 1070 âRessourcenkulturenâ
(DFG
Fandubbing
This chapter provides an overview of fandubbing, understood largely as a phenomenon encompassing a myriad of dubbing practices undertaken by amateur or non-expert users. The focus is placed on its origins, evolution and characteristics, and on the motivations of those involved in these underexplored fandom-related practices. This is achieved drawing on Bañosâ research on this topic, on the few academic publications dealing with this phenomenon, and on non-academic sources providing useful insight into these practices and revealing the point of view of the creators of fandubs. The chapter also highlights the differences between fandubs and official dubbing, and identifies areas of future research
Museum Audio Description: The Problem of Textual Fidelity
Museums present a myriad of source texts, which are often highly ambiguous. Yet Museum Audio Description (AD) is sited in an AD tradition which advocates objectivity. In screen AD, researchers have examined multiple aspects of the translation decisions facing the describer-translator, considering the ways in which AD is shaped by the demands of the source text, the impact of AD on the recipientâs experience and how these aspects may relate to objectivity. We examine the extent to which these decisions may apply to museum AD or differ in a museum setting. We argue that the notion of the âsource textâ for museums should be expanded beyond the visual elements of museumâs collections, encompassing the wider museum visiting experience. Drawing upon research from Museum Studies and Psychology, we explore the empirical evidence that characterises the experiences of mainstream sighted visitors and discuss the implications for museum AD. If it is to offer true access to the museum experience, then museum AD must consider not only the assimilation of visual information, but also the social, cognitive and emotional elements of visits. From this perspective, the emphasis is shifted from visual to verbal translation to the creative possibilities of re-creation in museum AD
Accessible opera : overcoming linguistic and sensorial barriers
The desire to make media available for all has been rapidly accepted and implemented by most European countries. Opera, as one of the many audiovisual representations, also falls under the category of production which needs to be made accessible and this article aims to analyse how opera has gone through a complete transformation to become a cultural event for all, overcoming not only linguistic but also sensorial barriers. The first part of the article analyses the various forms of translation associated with opera and the main challenges they entail. The second presents different systems used to make opera accessible to the sensorially challenged, highlighting their main difficulties. Examples from research carried out at the Barcelona's Liceu opera house are presented to illustrate various modalities, especially audio description. All in all, it is our aim to show how translated-related processes have made it possible to open opera to a wider audience despite some initial reluctance
Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Despite major advances in artificial intelligence (AI) for medicine and
healthcare, the deployment and adoption of AI technologies remain limited in
real-world clinical practice. In recent years, concerns have been raised about
the technical, clinical, ethical and legal risks associated with medical AI. To
increase real world adoption, it is essential that medical AI tools are trusted
and accepted by patients, clinicians, health organisations and authorities.
This work describes the FUTURE-AI guideline as the first international
consensus framework for guiding the development and deployment of trustworthy
AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and
currently comprises 118 inter-disciplinary experts from 51 countries
representing all continents, including AI scientists, clinicians, ethicists,
and social scientists. Over a two-year period, the consortium defined guiding
principles and best practices for trustworthy AI through an iterative process
comprising an in-depth literature review, a modified Delphi survey, and online
consensus meetings. The FUTURE-AI framework was established based on 6 guiding
principles for trustworthy AI in healthcare, i.e. Fairness, Universality,
Traceability, Usability, Robustness and Explainability. Through consensus, a
set of 28 best practices were defined, addressing technical, clinical, legal
and socio-ethical dimensions. The recommendations cover the entire lifecycle of
medical AI, from design, development and validation to regulation, deployment,
and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which
provides a structured approach for constructing medical AI tools that will be
trusted, deployed and adopted in real-world practice. Researchers are
encouraged to take the recommendations into account in proof-of-concept stages
to facilitate future translation towards clinical practice of medical AI
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