Bibliographic record
Abstract
This paper explores my ethnic claims in relation to the emergence of a recognizable Indian Hakka community in Toronto, Canada. I undertake an autoethnographic analysis of the changes and evolution in how I proclaim my identity; my initial reluctance to lay claim to my ethnic identity and its gradual consolidation are explored as an "intersectionality of struggles" of race and ethnic politics that framed my earlier hyper-vigilance over my difference and outsider status. An underlying concern is whether and how centering the self and the personal through an autoethnographic analysis can be a politically effective project toward building alliances instead of reproducing my own position of relative privilege. I use three moments of public proclamation of my ethnic identity to interrogate its development, to highlight its historicity, and to denaturalize it. This moves my représentational practice beyond the constraints of the postcolonial speaking position of the "native informant". The work of translation is treated as an encounter and a relationship between the self and the community rather than being simply the representative voice of the community.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.043 | 0.062 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".