Bibliographic record
Abstract
Abstract ‘Trinidadian Trinkets’ is a reflective piece about my experiences as a mixed person of colour and second generation Canadian. With my mother of English descent and my father Trinidadian, I’ve often struggled with identity in a culture that lays increasing importance on heritage and labels. I’ve felt mislabelled due to my light skin tone, but I’ve also often found that white crowds have never fully accepted me. As a Canadian-born woman who married a Belarussian-Ukrainian Canadian man, I’m not often confronted with my Trinidadian heritage. When both my grandparents on my father’s side passed away, I became aware of a part of me that felt like it was slipping away. I decided to honour my grandfather while simultaneously using his story and legacy to share my own. I try to incorporate how I still bear the markings and experiences that come from being Trinidadian despite being so far removed from those tropical shores. I use my physical features (with the exception of the soul) to exemplify these parts of me, these trinkets, which I treasure, and can trace all the way from my grandfather (whom I lovingly called “papa”) to my son, who is even lighter-skinned than me and has never met my grandparents.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".