Bibliographic record
Abstract
OcOr is a corpus of Occitan oral narratives. This corpus is one of the outputs of the project ExpressioNarration, financed by a Marie Sklodovska Curie Fellowship (2016-2018, n°655034). It includes three sub-corpora, constituted as follows: • OOT (Occitan, oral, traditional): stories drawn from fieldwork among native speakers in the Occitan domain, recorded by the COMDT (Conservatoire Occitan des Musiques et Danses Traditionnelles - http://www.comdt.org/), transcribed and digitised for the project by the researchers. • OWT (Occitan, written, traditional): published literary stories, digitised by and for the project by the researchers. These are stories collected from oral sources and produced in a publishable written version. • OOC (Occitan, oral, contemporary): stories recounted by contemporary artists, taken from existing recordings and two Toulouse storytelling events organised by the project in collaboration with the Institut d'Etudes Occitanes (IEO), in 2016. The stories were recorded during the events and subsequently transcribed and digitised by the researchers. The overall aim of the ExpressioNarration project was to use contemporary linguistic theory to explore the relationship between language and orality, with a specific focus on key temporal features of oral narrative in Occitan, including ‘tenses’, ‘connectives' and 'frame introducers'. These features were thus annotated in the three sub-corpora. All the sub-corpora are disseminated in XML format (TEI-P5) and PDF. Each story is available as an annotated XML document, an annotated PDF and a stripped PDF document. Full metadata appears in the Header of each XML document, with information on speakers (e.g. gender, age, place of origin, education, languages spoken), variety of Occitan (or dialect), authors/editorial information (in the case of OWT) and story-type when relevant (i.e. the Aarne Thompson category). For each sub-corpus, a user-friendly summary of this metadata is also available in an Excel spreadsheet: these are contained in the OcOr zipfile. The annotation system was designed by the researchers and is given in full in the Header of each XML document. For further information on the constitution of the corpus and discussion of the theoretical and methodological issues relating to data collection, digitisation and annotation, please read the following article in the journal Corpus, written by the researchers and entitled ‘Méthodologie pour la constitution d’un corpus comparatif de narration orale en Occitan : objectifs, défis, solutions’, available at: https://journals.openedition.org/corpus/3490.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".