It takes a village to write a book: Mapping anonymous contributions in Stephen Langton's Quaestiones Theologiae

• URL: http://arxiv.org/abs/2508.12830v1
• Date: Mon, 18 Aug 2025 11:13:45 GMT
• Title: It takes a village to write a book: Mapping anonymous contributions in Stephen Langton's Quaestiones Theologiae
• Abstract summary: This paper details the design of a study applying stylometric techniques of authorship attribution to a collection developed from reportationes.<n>It aims to uncover layers of editorial work and thus validate some hypotheses regarding the collection's formation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While the indirect evidence suggests that already in the early scholastic period the literary production based on records of oral teaching (so-called reportationes) was not uncommon, there are very few sources commenting on the practice. This paper details the design of a study applying stylometric techniques of authorship attribution to a collection developed from reportationes -- Stephen Langton's Quaestiones Theologiae -- aiming to uncover layers of editorial work and thus validate some hypotheses regarding the collection's formation. Following Camps, Cl\'erice, and Pinche (2021), I discuss the implementation of an HTR pipeline and stylometric analysis based on the most frequent words, POS tags, and pseudo-affixes. The proposed study will offer two methodological gains relevant to computational research on the scholastic tradition: it will directly compare performance on manually composed and automatically extracted data, and it will test the validity of transformer-based OCR and automated transcription alignment for workflows applied to scholastic Latin corpora. If successful, this study will provide an easily reusable template for the exploratory analysis of collaborative literary production stemming from medieval universities.

Related papers

• PaperClaw: Harnessing Agents for Autonomous Research and Human-in-the-Loop Refinement [22.280674906144622]
  PAPERCLAW is a harnessed multi-agent system that carries a project autonomously, from a field of study to a finished paper.<n>A full-lifecycle memory keeps each stage in a single living record, so a long run can be paused, inspected, and resumed without losing context.
  arXiv  Detail & Related papers  (2026-06-21T17:37:01Z)
• A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges [1.7724965898556184]
  This systematic review of the research literature on retrieval-augmented generation (RAG) provides a focused analysis of the most highly cited studies published between 2020 and 2025.
  arXiv  Detail & Related papers  (2025-08-08T15:37:14Z)
• Advancing Offline Handwritten Text Recognition: A Systematic Review of Data Augmentation and Generation Techniques [4.5220419118352915]
  This paper presents a survey of offline handwritten data augmentation and generation techniques.<n>We examine traditional augmentation methods alongside recent advances in deep learning.<n>We explore the challenges associated with generating diverse and realistic handwriting samples.
  arXiv  Detail & Related papers  (2025-07-08T12:03:58Z)
• Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models [53.17363502535395]
  Trustworthy language models should provide both correct and verifiable answers.<n>Current systems insert citations by querying an external retriever at inference time.<n>We propose Active Indexing, which continually pretrains on synthetic QA pairs.
  arXiv  Detail & Related papers  (2025-06-21T04:48:05Z)
• Large Language Models for Automated Literature Review: An Evaluation of Reference Generation, Abstract Writing, and Review Composition [2.048226951354646]
  Large language models (LLMs) have emerged as a potential solution to automate the complex processes involved in writing literature reviews.<n>This study introduces a framework to automatically evaluate the performance of LLMs in three key tasks of literature writing.
  arXiv  Detail & Related papers  (2024-12-18T08:42:25Z)
• Online Writer Retrieval with Chinese Handwritten Phrases: A Synergistic Temporal-Frequency Representation Learning Approach [53.189911918976655]
  We propose DOLPHIN, a novel retrieval model designed to enhance handwriting representations through synergistic temporal-frequency analysis.<n>We introduce OLIWER, a large-scale online writer retrieval dataset encompassing over 670,000 Chinese handwritten phrases from 1,731 individuals.<n>Our findings emphasize the significance of point sampling frequency and pressure features in improving handwriting representation quality.
  arXiv  Detail & Related papers  (2024-12-16T11:19:22Z)
• LitLLMs, LLMs for Literature Review: Are we there yet? [15.785989492351684]
  This paper explores the zero-shot abilities of recent Large Language Models in assisting with the writing of literature reviews based on an abstract.<n>For retrieval, we introduce a novel two-step search strategy that first uses an LLM to extract meaningful keywords from the abstract of a paper.<n>In the generation phase, we propose a two-step approach that first outlines a plan for the review and then executes steps in the plan to generate the actual review.
  arXiv  Detail & Related papers  (2024-12-15T01:12:26Z)
• A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution [57.309390098903]
  Authorship attribution aims to identify the origin or author of a document. Large Language Models (LLMs) with their deep reasoning capabilities and ability to maintain long-range textual associations offer a promising alternative. Our results on the IMDb and blog datasets show an impressive 85% accuracy in one-shot authorship classification across ten authors.
  arXiv  Detail & Related papers  (2024-10-29T04:14:23Z)
• The Languini Kitchen: Enabling Language Modelling Research at Different Scales of Compute [66.84421705029624]
  We introduce an experimental protocol that enables model comparisons based on equivalent compute, measured in accelerator hours. We pre-process an existing large, diverse, and high-quality dataset of books that surpasses existing academic benchmarks in quality, diversity, and document length. This work also provides two baseline models: a feed-forward model derived from the GPT-2 architecture and a recurrent model in the form of a novel LSTM with ten-fold throughput.
  arXiv  Detail & Related papers  (2023-09-20T10:31:17Z)
• Decoding the End-to-end Writing Trajectory in Scholarly Manuscripts [7.294418916091011]
  We introduce a novel taxonomy that categorizes scholarly writing behaviors according to intention, writer actions, and the information types of the written data. Motivated by cognitive writing theory, our taxonomy for scientific papers includes three levels of categorization in order to trace the general writing flow. ManuScript intends to provide a complete picture of the scholarly writing process by capturing the linearity and non-linearity of writing trajectory.
  arXiv  Detail & Related papers  (2023-03-31T20:33:03Z)
• PART: Pre-trained Authorship Representation Transformer [52.623051272843426]
  Authors writing documents imprint identifying information within their texts.<n>Previous works use hand-crafted features or classification tasks to train their authorship models.<n>We propose a contrastively trained model fit to learn textbfauthorship embeddings instead of semantics.
  arXiv  Detail & Related papers  (2022-09-30T11:08:39Z)
• Target-aware Abstractive Related Work Generation with Contrastive Learning [48.02845973891943]
  The related work section is an important component of a scientific paper, which highlights the contribution of the target paper in the context of the reference papers. Most of the existing related work section generation methods rely on extracting off-the-shelf sentences. We propose an abstractive target-aware related work generator (TAG), which can generate related work sections consisting of new sentences.
  arXiv  Detail & Related papers  (2022-05-26T13:20:51Z)
• Revise and Resubmit: An Intertextual Model of Text-based Collaboration in Peer Review [52.359007622096684]
  Peer review is a key component of the publishing process in most fields of science. Existing NLP studies focus on the analysis of individual texts. editorial assistance often requires modeling interactions between pairs of texts.
  arXiv  Detail & Related papers  (2022-04-22T16:39:38Z)
• What's New? Summarizing Contributions in Scientific Literature [85.95906677964815]
  We introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work. We extend the S2ORC corpus of academic articles by adding disentangled "contribution" and "context" reference labels. We propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs.
  arXiv  Detail & Related papers  (2020-11-06T02:23:01Z)
• SPECTER: Document-level Representation Learning using Citation-informed Transformers [51.048515757909215]
  SPECTER generates document-level embedding of scientific documents based on pretraining a Transformer language model. We introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction to document classification and recommendation.
  arXiv  Detail & Related papers  (2020-04-15T16:05:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.