Bibliographic record
Abstract
Ethnicity, the influence of stereotyping – whether it is overt or not - and personal identity intersect on a daily basis. But what is ethnicity? One could argue that it is a flexible idea, or as Weber (1968) notes, a matter of “subjective belief” (p. 389). According to Troper and Weinfeld (1987) “the definition of an ethnic group involves a sense of shared history, real or imagined (p. 106).” I include these two definitions of ethnicity because they both acknowledge the subjective nature of ethnic identity. Reflecting on subjectivity, however, raises questions concerning the interplay between ethnicity and stereotyping. To move beyond generalizations and explore the lived experiences and shared histories of various ethno cultural groups, one may investigate how groups came to form communities in Canada; and then consider the dynamics of the communities themselves. The aim of this paper is to explore the genesis and development of the Korean-Canadian community in Toronto, and reflect on how historical events and various social forces have impacted on its path, and contributed to its present state. The key segments of this paper include: Perspective; Toronto’s Korean-Canadian community; and institutional completeness.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".