Bibliographic record
Abstract
As an immigrant, I feel like I live in the hyphens negotiating between here, there and elsewhere. This identity crisis has aroused my post-colonial and feminist subjectivities to question how other I-m-migrant women navigate their hyphenated existence(s) with/in liminal spaces which are both home and not-home. The chapter engages with these issues in two intertwined ways. The first focuses on complicated conversations with eleven Indo-Canadian women and explores their often silent and unheard stories of their live(d) experiences in the hyphens. The second is an account of my transdisciplinary auto/ethnographical Ph.D. research which partakes of multiple disciplines, including feminist research, cultural and post-colonial Studies, narrative inquiry, poetic inquiry and performance ethnography. Written as a storied quilt, this chapter braids my own and my participants’ live(d) experiences together to showcase our hyphenated experiences. This literary métissage provides an educational and transdisciplinary approach which speaks into the potential of writing and knowledge not just to represent but to question and transform oppressive social conditions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".