Some Good Reasons for Renaming Places, and Some not so Good Ones: a Cross-Cultural Sketch. In Honour of Canada’s 150th Birthday and the Year of Reconciliation
Bibliographic record
Abstract
A CROSS-CULTURAL SKETCH In honour of Canada's 150 th Birthday and the Year of ReconciliationThe note focuses on the initiative of renaming some places in Canada to celebrate the year of Canada's 150 th anniversary, as well as the Year of Reconciliation (2017).The initiative aims at revitalizing the original names given by the First Nations, i.e. coming from the Cree, Salish and other Aboriginal languages.The author cites examples proving that such initiatives are not always shared by the public due to the pronunciation diffi culties new names may cause (such is the renaming of Mount Douglas to Saanich Pkols [pkˀals] and Mount Newton to Saanich ŁÁU,WELṈEW_, that had been in the works for quite a while before 2017).In some other cases, the renaming turns out to be controversial, inconsistent or incomplete: like Fort Amherst that still retains its name after an 18 th -century British Army Offi cer guilty of extirpation of indigenous people (Parks Canada having opposed the removal of the name Amherst since 2008), or Fushimi Lake, formerly known as Pewabiska by its Ojibwa / Cree origins, and whose name was changed in the early 20 th century to commemorate the visit of prince Hiroyasu Fushimi (some other places in his honour being renamed as far back as during World War II).The author also points out that the need for renaming has gone beyond the concern of the First Nations and presently affects some groups of immigrants, which is the case with the name of Berlin (Kitchener) in Ontario.K e y w o r d s: place names of Canada, Salish languages, Cree languages, Mount Douglas, Mount Newton, Tsilhqot'in.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".