Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1711.04452

Digitising Cultural Complexity: Representing Rich Cultural Data in a Big Data environment

Jennifer Edmond·Georgina Nugent Folan
View original source ↗Read source document ↗Export metadata ↓

Abstract

One of the major terminological forces driving ICT integration in research today is that of "big data." While the phrase sounds inclusive and integrative, "big data" approaches are highly selective, excluding input that cannot be effectively structured, represented, or digitised. Data of this complex sort is precisely the kind that human activity produces, but the technological imperative to enhance signal through the reduction of noise does not accommodate this richness. Data and the computational approaches that facilitate "big data" have acquired a perceived objectivity that belies their curated, malleable, reactive, and performative nature. In an input environment where anything can "be data" once it is entered into the system as "data," data cleaning and processing, together with the metadata and information architectures that structure and facilitate our cultural archives acquire a capacity to delimit what data are. This engenders a process of simplification that has major implications for the potential for future innovation within research environments that depend on rich material yet are increasingly mediated by digital technologies. This paper presents the preliminary findings of the European-funded KPLEX (Knowledge Complexity) project which investigates the delimiting effect digital mediation and datafication has on rich, complex cultural data. The paper presents a systematic review of existing implicit definitions of data, elaborating on the implications of these definitions and highlighting the ways in which metadata and computational technologies can restrict the interpretative potential of data. It sheds light on the gap between analogue or augmented digital practices and fully computational ones, and the strategies researchers have developed to deal with this gap. The paper proposes a reconceptualisation of data as it is functionally employed within digitally-mediated research so as to incorporate and acknowledge the richness and complexity of our source materials.

Explore related subjects

cs.CLcs.CYcs.DBcs.DL

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jennifer Edmond, Georgina Nugent Folan. 2017-11-13. Digitising Cultural Complexity: Representing Rich Cultural Data in a Big Data environment. https://arxiv.org/abs/1711.04452

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

DA-Cramming: Enhancing Cost-Effective Language Model Pretraining with Dependency Agreement Integration

Pretraining language models is still a challenge for many researchers due to its substantial computational costs. As such, there is growing interest in developing more affordable pretraining methods. One notable advancement in this area is the Cramming technique (Geiping and Goldstein, 2022), which enables the pretraining of BERT-style language models using just one GPU in a single day. Building on this innovative approach, we introduce the Dependency Agreement Cramming (DA-Cramming), an efficient framework that integrates information about dependency agreements into the pretraining process. Unlike existing methods that leverage similar semantic information during finetuning, our approach represents a pioneering effort focusing on enhancing the foundational language understanding with semantic information during pretraining. We meticulously design a dual-stage pretraining work flow with four dedicated submodels to capture representative dependency agreements at the chunk level, effectively transforming these agreements into embeddings to benefit the pretraining. Extensive empirical results demonstrate that our method significantly outperforms previous methods across various tasks.

cs.CL↗
arXiv2026

BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?

We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generating code with specified time and space complexities. This benchmark addresses the gap in current evaluations that often overlook the ability of models to comprehend and produce code constrained by computational complexity. BigO(Bench) includes tooling to infer the algorithmic complexity of any Python function from profiling measurements, including human- or LLM-generated solutions. BigO(Bench) also includes of set of 3,105 coding problems and 1,190,250 solutions from Code Contests annotated with inferred (synthetic) time and space complexity labels from the complexity framework, as well as corresponding runtime and memory footprint values for a large set of input sizes. We present results from evaluating multiple state-of-the-art language models on this benchmark, highlighting their strengths and weaknesses in handling complexity requirements. In particular, token-space reasoning models are unrivaled in code generation but not in complexity understanding, hinting that they may not generalize well to tasks for which no reward was given at training time.

cs.CL↗
arXiv2026

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking

The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM safety evaluation benchmarks are proposed. However, existing benchmarks generally rely on labor-intensive manual curation, which causes excessive time and resource consumption. They also exhibit significant redundancy and limited difficulty. To alleviate these problems, we introduce SafetyFlow, the first agent-flow system designed to automate the construction of LLM safety benchmarks. SafetyFlow can automatically build a comprehensive safety benchmark in only four days without any human intervention by orchestrating seven specialized agents, significantly reducing time and resource cost. Equipped with versatile tools, the agents of SafetyFlow ensure process and cost controllability while integrating human expertise into the automatic pipeline. The final constructed dataset, SafetyFlowBench, contains 23,446 queries with low redundancy and strong discriminative power. Our contribution includes the first fully automated benchmarking pipeline and a comprehensive safety benchmark. We evaluate the safety of 49 advanced LLMs on our dataset and conduct extensive experiments to validate our efficacy and efficiency.

cs.CL↗