Some Challenges of Names Recognition: \nThe Ontario Geographic Names Board, Canada, 2000–2007
Bibliographic record
Abstract
Canada was one of the first countries to establish a geographical names authority and has participated in the \nwork of standardization at the United Nations since the first conference in 1967. Over the past forty years \nthe approval of geographical names in Canada has been primarily the responsibility of the provinces and, \nfor a shorter time, the territories. \nThe names authority for the Province of Ontario, the Ontario Geographic Names Board (OGNB), \ncomprises seven members (including representatives from Ontario First Nations, and the province’s \nEnglish- and French-speaking communities). Between 2000 and 2007, the OGNB considered some 380 \nnames submissions, recommending approximately 330 for official recognition and general dissemination. \nThis paper looks at some of the main challenges to the Board during this period. Among the questions \nconsidered were issues relating to such themes as commemorative naming, urban community naming, and \nhandling existing names considered derogatory. Sometimes bearing on the Board’s approaches to these \nissues were the need for names for emergency reference purposes (911 dialling), a sparse population in \nnorthern areas of the province to support local usage, and conflicting submissions to replace derogatory \nnames. Issues, approaches, examples, and preliminary guidelines are presented.
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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 | 0.024 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.033 | 0.011 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".