Distance intertextuelle et classement des textes d'après leur structure : méthodes de découpage et analyses arborées
Bibliographic record
Abstract
The intertextual distance is often calculated by studying frequencies. As part of a research project on texts topology, we will evaluate here the efficiency of one of the methods we described in 2004. For the calculation of the intertextual distance, this method tries to take into account the text linearity by a text segmentation into a succession of n contiguous parts. By combining this method with tree analyses, we will study the distribution of a linguistic phenomenon (in this case, a tense) in the general framework of texts. Our long term aims are to evaluate the variability of linguistic phenomena correlation according to the parts of the text (introduction, text body, conclusion, etc) and to calculate distances between texts according to their structure. By studying a corpus based on a latin historical lemmatized texts databank, we will try here to establish the limits of the size heterogeneousness of the analysed texts and to evaluate the efficiency of the method according to the selected parameters.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".