The Evolution of Inuktut Dictionary-Making: From Historical Documentation to Inuit Authorship and Collaborations
Bibliographic record
Abstract
ABSTRACT: This paper examines the history of dictionary-making for the Inuktut (Inuit) language in Canada from the nineteenth century until today, ranging from those created by missionaries and linguists to projects led by Inuit or through collaborations between Inuit and non-Inuit language specialists. We propose four stages or phases of Inuktut dictionary-making, adapting Czaykowska-Higgins's (2009) model of linguistic fieldwork. The first phase, Inuit as informants (working on Inuit and Inuktut language), focuses on the work of missionaries, linguists, and anthropologists who gathered wordlists and learned the language during their sojourns in the Arctic from the eighteenth century well into the 1970s. The second phase, Inuit as beneficiaries (working for Inuit), includes the awareness to document language use by Inuit in the development of literacy, publications, education, and revitalization efforts in general. The third phase involves Inuit as primary authors (work by Inuit) in dictionary-making projects. Lastly, there are new collaborative projects that involve working with Inuit as partners (working with Inuit), particularly in the fields of digital mapping, online dictionaries, and digital databases. We trace all four of these dictionary-making trajectories, recognizing that some of these phases overlap (temporally and functionally) and highlighting the political and economic goals and agency of Inuit over the past two centuries to retain and secure more control over their land and languages.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".