Digital Implementation of Doteli Dictionary: A Tri-Lingual Dictionary for Doteli Language
Bibliographic record
Abstract
This research article discusses the development and implementation of a digital Doteli dictionary, designed to bridge the linguistic gap between Doteli, Nepali, and English. The trilingual dictionary app operates on Android platforms and offers functionalities such as word meaning searches, antonym/synonym retrieval, pronunciation guides, language translation, and the ability to bookmark words. Physical dictionaries have several limitations, including bulkiness, low portability, time-consuming searches, difficulty in modification, limited accessibility, and the absence of pronunciation features. According to the literature, no digital version of the Doteli dictionary has been developed to date. This research aims to address these limitations by providing a digital version of the Doteli dictionary that includes all the aforementioned features. Drawing inspiration from traditional dictionaries, the digital version seeks to preserve, promote, and make the linguistic richness of the Doteli language more accessible. The development and testing of the app follow the System Development Life Cycle (SDLC) approach, with all sample data sourced from physical dictionaries. The system has been successfully implemented and installed on Android-based mobile devices using Java programming. This work is beneficial for language learners, linguists, researchers, teachers, students, and developers.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".