Bibliographic record
Abstract
Language is the root of cultural identity; it is important for language survival as it is important for communities to have community members who are proud to speak their Indigenous language and be Mi’kmaq, Maliseet, First Nation, Metis, Inuit, Aboriginal, Indigenous, and Native. Speaking with my elders about nurturing Mi’kmaq language and culture is helping me to understand how we can have more Mi’kmaw speakers and healthy communities. Interviewing my elders and hearing her stories “leaves [me] with a sense of purpose, pride, and gives [me] guidance and direction. [Her] stories are of survival and resistance” (Thomas, 2005, p. 238).Mi’kmaq communities without fluent Mi’kmaq speakers can use comprehensive interactive talking dictionaries and other digital tools for revitalizing the language (Nathan, 2007; O’Donnell et al, 2010). Dictionaries are documentation devices for Mi’kmaq resurgence and “are being reconceived and explored for pedagogical potential through the use of multimedia technology” (Korne, 2009, p. 141). This is an example of the potential power of Information and Communication Technology (ICT) and how ICT can be the first step to maintaining, recovering, and reclaiming our Mi’kmaq language and Indigenous paradigms because language carries culture (Smith, 2012; Wilson, 2008; Grande, 2004).
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.040 | 0.048 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".