Bridging the Digital Divide in Sri Lanka: Some Challenges and Opportunities in using Sinhala in ICT
Bibliographic record
Abstract
The "digital divide" is the gap in technology usage and access. The digital divide has been investigated by scholars [1] and policy makers [2] mainly as an economy-specific issue that permeates the population across all demographic profiles, such as income, gender, age, education, race, and region, but not specific to the languages of different communities. The lack of native language driven ICT is a major conducive factor in digital divide. Sinhala writing system used in Sri Lanka is a syllabic writing system derived from Brahmi which consist of vowels, consonants, diacritical marks and special symbols constructs. Several of these constructs are combined to form complex ligatures. The total number of different glyphs is almost close to 2300 in Sinhala language. Thus, all computer equipments that support Sinhala language needs to support a greater degree of complexity in both display and printing with near minimal changes to the keyboard or the input systems. In this paper we discuss (1) historical background of the Sinhala writing system, (2) Sinhala scripts’ characteristics and complexities and illustrate (3) how Sinhala computing technology has evolved over the last quarter century. Major steps are marked by the design of character code standards as a corner stone of whole architecture for text processing. A case described in this article of “Digital Inclusion” shows how small communities of non-Roman script users can connect to the Romanized system dominated cyberspace.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".