Bibliographic record
Abstract
In Digital Humanities the task of “text modelling” has been recognised and successfully treated in the last decades. But indeed every use of digital text processing software, even the most naive one, is already a kind of text modelling activity. In many realms of daily practice this is executed unknowingly and without theoretic reflection, using digital text processing systems merely as “comfortable typewriters”. Then the structure of the out-coming models is determined only by the applied software. To really exploit the benefits of automated text processing in any realm, their use must change to a theory and practice of text modelling. This requires to explore and make explicit the mathematical structure of the possible text models, and the restrictions imposed on them a priori by the involved technical tools. Those can become crucial especially. when translating a text between two formats – a quite frequent task with surprising pitfalls. This article gives a systematic and exhaustive survey of the technically determined structural properties of text models. It lists the abstract requirements on modelling tools for ensuring satisfactory flexibility, and compares ten different commonly used text modelling frameworks. Résumé Les systèmes de traitement de texte numériques s’utilisent, dans la majorité des cas, simplement comme des « machines à écrire confortables », surtout dans les Humanités. Pour profiter véritablement des avantages du traitement de texte automatisé, surtout au niveau conceptuel, l’usage de ces systèmes de traitement doit changer vers une théorie et pratique de modélisation de texte. Pour cela, il faut explorer et rendre explicite la structure mathématique de modèles de texte possibles, ainsi que les restrictions imposées a priori sur ces systèmes par les outils techniques impliqués. Celles-là peuvent devenir essentielles, particulièrement lorsque l’on convertit un texte entre deux formats – ce qui est une tâche très fréquente avec des écueils surprenants. Cet article offre une étude systématique et exhaustive des caractéristiques structurelles de modèles de texte. Il énumère les exigences abstraites d’outils de modélisation nécessaires pour garantir une flexibilité satisfaisante et il compare dix différents cadres de modélisation de texte fréquemment utilisés. Mots-clés: Traitement de texte; Type de document; Enquête sur les normes; Modélisation mathématique
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".