Seeing the Heiltsuk orthography from font encoding through to Unicode: A case study using convertextract
Bibliographic record
Abstract
Across the world's languages and cultures, most writing systems predate the use of computers. In the early years of ICT, standards and protocols for encoding and rendering the majority of the world's writing systems were not in place. The opportunity to deploy less-commonly used orthographies in cross-platform digital contexts has steadily increased since Unicode became the most widely used encoding on the web in late 2007 (Davis, 2008). But what happens to resources that were developed before Unicode standards became widespread? While many tools have been created to address this problem and other issues related to transliteration and character level substitutions, 1 this paper describes the process undertaken for the Indigenous and endangered Heiltsuk (Wakashan) language, and outlines a tool (Convertextract) that was designed to convert not only plain text, but also Microsoft Office (pptx, xlsx, docx) documents with the goals of updating and upgrading pre-existing digital textual resources to Unicode standards, and thus preserving the knowledge they contain for both the present and the future.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".