The Negotiation of Personal Names: An Exploration of Educators’ Usage and Pronunciation of Student Names in K-12 and Higher Education
Bibliographic record
Abstract
The following study provides a critical examination of how educators in Ontario navigate the political and social aspects of personal names in education, how they use student names, as well as how they (struggle to) pronounce student names. Seeking strategies to prevent offending students, this study involved ten personal interviews with educators, and examines educators’ practices and experiences related to personal names. It further involves scholarship, educational policies’ texts, personal observations, and documents pertaining to K-12 student-teacher relations, shining light on the impact of acculturation through the un-naming, misnaming, and re-naming of students, and how these practices contradict Canadian policies of multiculturalism and Ontario school boards’ human rights and social justice education policies. Based on these conclusions, this study proposes the implementation of a technological tool, NameCoach, to support educators with accurate name pronunciations whilst maintaining the importance of developing positive relationships and community building in various educational spaces.
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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".