Deep Data Example: Zbiva, Early Medieval Data Set for the Eastern Alps

Štular, Benjamin; Belak, Mateja

doi:10.1163/24523666-bja10024

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…In our case study, pertinent to Q2, the data were the documentation of the excavations of 1106 archaeological sites. The information was summaries of the excavation reports and scientific publications on these 1106 sites curated in the structured database Zbiva, available on the public web since 2016 [112]. Knowledge was the scholarly articles discussing and/or analysing the migration of the Alpine Slavs.…”

Section: Discussion: Data To Knowledgementioning

confidence: 99%

Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots’ Proficiency and Originality in Scientific Writing for Humanities

Lozić,

Štular

2023

Future Internet

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Historically, mastery of writing was deemed essential to human progress. However, recent advances in generative AI have marked an inflection point in this narrative, including for scientific writing. This article provides a comprehensive analysis of the capabilities and limitations of six AI chatbots in scholarly writing in the humanities and archaeology. The methodology was based on tagging AI-generated content for quantitative accuracy and qualitative precision by human experts. Quantitative accuracy assessed the factual correctness in a manner similar to grading students, while qualitative precision gauged the scientific contribution similar to reviewing a scientific article. In the quantitative test, ChatGPT-4 scored near the passing grade (−5) whereas ChatGPT-3.5 (−18), Bing (−21) and Bard (−31) were not far behind. Claude 2 (−75) and Aria (−80) scored much lower. In the qualitative test, all AI chatbots, but especially ChatGPT-4, demonstrated proficiency in recombining existing knowledge, but all failed to generate original scientific content. As a side note, our results suggest that with ChatGPT-4, the size of large language models has reached a plateau. Furthermore, this paper underscores the intricate and recursive nature of human research. This process of transforming raw data into refined knowledge is computationally irreducible, highlighting the challenges AI chatbots face in emulating human originality in scientific writing. Our results apply to the state of affairs in the third quarter of 2023. In conclusion, while large language models have revolutionised content generation, their ability to produce original scientific contributions in the humanities remains limited. We expect this to change in the near future as current large language model-based AI chatbots evolve into large language model-powered software.

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Section: Discussion: Data To Knowledgementioning

confidence: 99%

Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots’ Proficiency and Originality in Scientific Writing for Humanities

Lozić,

Štular

2023

Future Internet

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“…BaDACor is also included in this specific international archaeology initiative. ARIADNEplus established a comprehensive and interconnected digital infrastructure that supports research, fosters collaboration between scholars and institutions, and enhances the dissemination of archaeological knowledge to diverse audiences (Niccolucci and Richards 2013;Štular 2019). ARIADNEplus promotes the sustainability of the infrastructure by instituting policies and procedures that ensure the long-term preservation and accessibility of archaeological data, as well as fostering the development of a community of practice focused on exchange, management, and administration of data.…”

Section: Integration Into Local/global Systemsmentioning

confidence: 99%

“…, and EAMENA(McCoy 2020;Niccolucci and Richards 2013;Pleterski 2016;Štular 2019;Wells et al 2014;Zerbini 2018). Using points for describing site locations allows interoperability between the various software program implementations.…”

mentioning

confidence: 99%

Towards an Open Digital Ecosystem for Archaeology in South America: The BADACor (Córdoba Archaeological Sites Database) as a case of an open digital archaeological source for heritage management in central Argentina

Izeta,

Cattáneo

2023

Internet Archaeol.

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BaDACor is a database that contains a comprehensive inventory of archaeological sites located in the province of Córdoba, Argentina. The creation of this database was the result of a top-down approach, which involved the collaboration of decision-makers and professionals from the academic and state-governmental sectors. Furthermore, the database has also been utilised in a bottom-up approach, whereby interest groups and citizens concerned with heritage preservation have made use of it. This has been particularly important in light of the construction of Highway 38, which has resulted in damage to natural habitats and the destruction of territories of communities with traditional ways of life. Additionally, the construction of the highway has also endangered the integrity of ancestral territories loaded with symbolism for aboriginal communities. BaDACor has been employed in legal claims in cases of conflict with the state, and has proved to be an invaluable tool for heritage management. This is especially significant for local communities and indigenous groups who have historically had their heritage desecrated, destroyed, and hidden. The availability of BaDACor on different platforms has facilitated better access to information while also ensuring the preservation of digital data. The use of digital media has been reinforced through talks, conferences, and meetings with stakeholders to ensure that the voices of affected communities are heard in decision-making processes.

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“…Because the data set is the result of four decades of deliberate scholarly work and attentive curation, it is best described by the concept of Deep Data. The Deep Data approach is one in which we make full use of all the information available in the data to gain the knowledge [25], cf. [26].…”

Section: Data Set and Study Areamentioning

confidence: 99%

“…The data are described in more detail in the data paper [25] and are openly accessible via the Zenodo repository [32].…”

Section: Plos Onementioning

confidence: 99%

Migration of Alpine Slavs and machine learning: Space-time pattern mining of an archaeological data set

Štular¹,

Lozić²,

Belak³

et al. 2022

PLoS ONE

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The rapid expansion of the Slavic speakers in the second half of the first millennium CE remains a controversial topic in archaeology, and academic passions on the issue have long run high. Currently, there are three main hypotheses for this expansion. The aim of this paper was to test the so-called “hybrid hypothesis,” which states that the movement of people, cultural diffusion and language diffusion all occurred simultaneously. For this purpose, we examined an archaeological Deep Data set with a machine learning method termed time series clustering and with emerging hot spot analysis. The latter required two archaeology-specific modifications: The archaeological trend map and the multiscale emerging hot spot analysis. As a result, we were able to detect two migrations in the Eastern Alps between c. 500 and c. 700 CE. Based on the convergence of evidence from archaeology, linguistics, and population genetics, we have identified the migrants as Alpine Slavs, i.e., people who spoke Slavic and shared specific common ancestry.

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Deep Data Example: Zbiva, Early Medieval Data Set for the Eastern Alps

Cited by 6 publications

References 12 publications

Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots’ Proficiency and Originality in Scientific Writing for Humanities

Fluent but Not Factual: A Comparative Analysis of ChatGPT and Other AI Chatbots’ Proficiency and Originality in Scientific Writing for Humanities

Towards an Open Digital Ecosystem for Archaeology in South America: The BADACor (Córdoba Archaeological Sites Database) as a case of an open digital archaeological source for heritage management in central Argentina

Migration of Alpine Slavs and machine learning: Space-time pattern mining of an archaeological data set

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