Modelling of a Heterogeneous Corpus: The Example of Chapbook Literature
Bibliographic record
Abstract
This article proposes an analysis of Spanish chapbook literature from the digital perspective. It provides a systematic study of the metadata and services selected by seventeen digital libraries to model their collections.In the first part, we propose an overview of those libraries. Their great variety reflects the heterogeneity of this popular literature, which is at the margin of studies on printed productions, and escapes the classifications traditionally used for books. In the second part, we describe four different strategies used by digital libraries to model this type of content, focusing either 1) on the document as an archive to be preserved, 2) on the document as the result of an editorial process, 3) on the text, or 4) on the illustrations. Our objective is thus to outline a digital model for these types of documents and to help future projects in defining their own offer of services.This article is part of the research project "Untangling the cordel / Démêler le cordel / Desenrollando el cordel (2020-2023)" financed by the Philanthropic Sandoz-Monique de Meuron Family Foundation and directed by the professor Constance Carta (University of Geneva).Cet article propose une analyse de la littérature de colportage espagnole au prisme du numérique, en étudiant de manière systématique les métadonnées et les services sélectionnés par dix-sept bibliothèques numériques pour modéliser leurs collections.Dans un premier temps, nous proposons un panorama de ces bibliothèques, dont la grande variété reflète celle de cette littérature populaire, à la marge des études sur les productions imprimées et qui échappe aux classifications traditionnellement employées pour le livre. Dans un second temps, nous décrivons quatre stratégies différentes employées par les bibliothèques numériques pour modéliser ce type de contenus, en mettant l’accent soit sur le document en tant qu’archives à conserver, soit sur le document en tant que résultat d’un processus éditorial, soit sur le texte, soit sur l’illustration. Notre objectif est ainsi d’esquisser les contours d’un modèle numérique pour ce type de documents et d’aider de futurs projets dans la définition de leur propre offre de services.Cet article s’inscrit dans le cadre du projet de recherche « Démêler le cordel / Desenrollando el cordel / Untangling the cordel (2020-2023) » financé par la Fondation philanthropique Famille Sandoz-Monique de Meuron et dirigé par la professeure Constance Carta (Université de Genève).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".