Nêhiyawi-pîkiskwêwina maskwacîsihk : Spoken Dictionary of Maskwacîs Cree
Bibliographic record
Abstract
ABSTRACT: This paper details the development of nêhiyawi-pîkiskwêwina maskwacîsihk: Spoken Dictionary of Maskwacîs Cree (in progress). Since 2014, this joint project between the Maskwacîs Education and Schools Commission (MESC) and the Alberta Language Technology Lab (ALTLab) has sought to record carefully pronounced, isolated spoken audio for the approximately 9,000 entries in the Maskwacîs Dictionary of Cree Words (Maskwachees Cultural College 2009), as well as to fill lexical gaps through elicitation, to record example sentences for as many of these entries as possible, and to make these recordings publicly available online. Between 2014 and 2018, approximately 700 hours of audio and close to 120,000 recordings for 20,300 carefully spoken word and phrase types were gathered in elicitation sessions. After extracting and annotating the relevant Cree vocabulary, these audio clips were compiled in a novel, publicly accessible online Speech Database as well as through itwêwina , the intelligent bilingual online Cree–English dictionary. The entries in this database are currently in the process of orthographic standardization, gloss standardization, and linguistic analysis. Simultaneously, native speakers of Cree are re-reviewing the database's entries to ensure pronunciation quality and verify definitions where needed. In this paper, we discuss the origins of this project; the original elicitation sessions; the postprocessing, standardization, and validation of the recordings; and means by which these recordings can be publicly accessed online.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".