Mapping Experience: Age and Indigeneity as Mediating Factors in Users’ Experiences with the Algonquian Linguistic Atlas
Bibliographic record
Abstract
To understand how effectively digital maps of Indigenous languages engage a variety of audiences, a mixed-methods user study focused on the experiences of 23 Indigenous and non-Indigenous users aged under and over 30 from a Canadian university as they navigated an online Canadian Indigenous language atlas by completing a series of tasks. An evaluative component assessed the efficacy of the study itself in measuring such experiences. Indigenous participants found the atlas more relevant and useful and focused more on its linguistic content, while non-Indigenous participants focused on the layout and structure of the atlas’s framework. Digital language atlases can better address Canadian Indigenous populations by emphasizing multimodal representations of linguistic content, with easily accessible links to additional resources from the communities represented. While the study did capture multiple dimensions of user experience, low Indigenous participation decreased the efficacy of comparative statistical analyses. Future research can improve Indigenous representation by focusing on recruitment methods that engage and are relevant to Indigenous populations.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".