Bibliographic record
Abstract
wihtaskamihk kîkâc kahkiyaw nîhîyaw pîkiskwîwina î namatîpayiwa wiya môniyâw onîkânîwak kayâs kâkiy sihcikîcik ka nakinahkwâw nîhiyaw osihcikîwina. atawiya anohc kanâta askiy kâpimipayihtâcik î tipahamok nîhiyaw awâsisak kakisinâmâkosicik mîna apisis î tipahamok mîna ta kakwiy miciminamâ nîhiyawîwin. namoya mâka mitoni tapwîy kontayiwâk î nîsohkamâkawinaw ka miciminamâ nipîkiskwîwinân. pako kwayas ka sihcikiy kîspin tâpwiy kâ kakwiy miciminamâ nîhîyawîwin îkwa tapwiy kwayas ka kiskinâhamowâyâ kicowâsim’sinân. ôma masinayikanis îwihcikâtîw tânihki kîkâc kâ namatîpayicik nipîkiskwîwinân îkwa takahki sihcikîwina mîna misowiy kâ apicihtâcik ka pasikwînahkwâw nîhiyawîwin nanântawisi. (Translated into Nîhîyawîwin (Northern Cree) [crk], a language of Canada, by Art Napoleon) Canada’s Indigenous languages are at risk of extinction because of government policies that have actively opposed or neglected them. A few positive steps by government include investments in Aboriginal Head Start, a culturally based early childhood program, as well as a federal Aboriginal Languages Initiative. Overall, however, government and public schools have yet to demonstrate serious support for Indigenous language revitalization. Language-in-education policies must address the historically and legislatively created needs of Indigenous Peoples to increase the number of Indigenous language speakers and honor the right of Indigenous children to be educated in their language and according to their heritage, with culturally meaningful curricula, cultural safety, and dignity. This chapter describes how Canada arrived at a state of Indigenous language devastation, then explores some promising developments in community-driven heritage language teaching, and finally presents an ecologically comprehensive strategy for Indigenous language revitalization that draws on and goes beyond the roles of formal schooling. It’s been a cold 130 years for Canada’s first languages, and the thaw is still awaited. (Fettes & Norton, 2000: 29)
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".