Encoding Disappearing Characters: The Case of Twentieth-Century Japanese-Canadian Names
Bibliographic record
Abstract
The Landscapes of Injustice project seeks to encode mid-twentieth-century documents by and about the Japanese-Canadian community so they are accessible to modern audiences. The fundamental problem is that some of the kanji used at that time have been replaced since then by different kanji, and others have been removed from lists of formally acceptable characters. This report documents our efforts with two technologies designed to address this situation. The first is the Standardized Variation Sequence (SVS) feature of Unicode. Our work revealed that this set of variation sequences does not completely cover the old and new glyph pairs identified by the Japanese authorities, and that the pairs formally identified by the Japanese authorities do not completely cover all the new glyph forms in general use. We turned to TEI’s , , and elements as a second technology to augment the support provided by Unicode. Lastly, we dealt with the issue of finding suitably qualified people to do the markup. The result is markup which retains the original glyphs and relates them to the modern glyphs, so that in our output products we will be able to support search and display using either form of the glyph.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".