Modern Wendat Lexicography: Using XML to Reflect the Grammar and Lexicon of an Iroquoian Language
Bibliographic record
Abstract
ABSTRACT: Building dictionaries with tools and methods emerging from Eurocentric traditions has proved problematic for Indigenous languages. We are building a dictionary for Wendat, an Iroquoian language formerly known as Huron that is being reawakened in Wendake, Québec. There are twelve manuscript dictionaries and lexicons for Wendat, created by missionaries during the seventeenth and eighteenth centuries. We are encoding the manuscripts using a standard Text Encoding Initiative (TEI) schema. However, when we came to create and encode a modern reconstructed Wendat dictionary, we were overly constrained by Eurocentric structures and assumptions inherent to TEI. Building our own custom XML schema allows us to better reflect Wendat grammar, responding to community needs and our evolving understandings of the language. This article describes the development of this schema, based on analysis of the archival documentation and related languages. Through this discussion, we will exemplify the schema we built and address the points of friction between TEI and Wendat grammatical structures. Our custom schema enables us to elegantly and economically represent exactly what our analysis of the language reveals, capturing elements of the language such as event-verb consequentiality, conjugation class, and stems, while avoiding incompatible elements and assumptions.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".