Digital Archiving by Nigerian and Foreign Authors in a Low Resource Context: A Content Analysis of Publications on Natural Language Processing of Nigerian Languages
Bibliographic record
Abstract
This study investigated if there is a difference in the number of articles, datasets and computer codes that foreign and Nigerian authors of scientific publications on natural language processing (NLP) of Nigerian languages deposited in digital archives. Relevant articles were systematically retrieved from Google, Web of Science and Scopus. Authorship type and data archiving information was extracted from the full text of the relevant publications. Result shows that papers with foreign authorship (80.4%) published their articles in non-commercial repositories, more than papers with Nigerian authorship (55.3%). Similarly, few papers with foreign authorship deposited research data (19.1%) and computer codes (10.4%), while none of the papers with Nigerian authorship did. It was recommended that librarians in Nigeria should create awareness on the benefits of digital archiving and open science. Cette étude a eximané les différences dans le nombre d'articles, d'ensembles de données et de codes informatiques dans les articles scientifiques sur le traitement du langage naturel que les auteurs nigériens et les auteurs étrangers ont soumis dans les dépôts d'autoarchivage. Les articles pertinents ont été systématiquement extraits de Google, Web of Science et Scopus. Les informations relatives au type d'auteur et à l'archivage des données ont été extraites du texte intégral des publications pertinentes. Les résultats montrent que les articles écrits par des auteurs étrangers ont davantage publié leurs articles dans des dépôts non commerciaux (80,4%) que les auteurs nigériens (55,3%). Peu d'auteurs étrangers ont déposé des données de recherche (19,1%) et des codes informatiques (10,4%) tandis qu'aucun auteur nigérien ne l'a fait. Ces résultats démontrent l'importance de la sensibilisation aux avantages des dépôt d'archivage et de la science ouverte pour les bibliothécaires nigériens.
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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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".