Principles and Practicalities of Corpus Design in Language Retrieval: Issues in the Digitization of the Beynon Corpus of Early Twentieth-Century Sm’algyax Materials
Bibliographic record
Abstract
This paper describes a pilot project to develop a machine-readable corpus of early twentieth-century Sm’algyax texts from a large collection of handwritten manuscripts collected by the Tsimshian ethnographer and chief William Beynon. The project seeks to ensure that the materials produced are maximally accessible to the Tsimshian community. It relates established principles for corpus design to practical issues in language retrieval, recognizing that the corpus will likely function as an intermediate stage between the original manuscripts and any language materials developed by the community. The paper is addressed primarily to linguists working on language retrieval projects but may also be of use to communities who are working with linguists, as it provides insight into the concerns and preoccupations that linguists bring to such tasks.
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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 | 0.142 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".