Bibliographic record
Abstract
How can I identify as an Aboriginal woman if I don't look like one? If I’m only a small percentage in the eyes of others? How do I have the right? It’s the same question I asked myself when I identified with my Latina heritage, with my white heritage. I mean I am French and Irish, I am Venezuelan, I am Cree, Ojibwa and Métis as much as I am Trinidadian, and yet I always feel like I’m a fraud. Although I look black, I don't feel black enough to identify as black. Although I can pass as Latina, I don't feel as if I’m enough. Although I know so much about my Aboriginal culture and I do identify as Aboriginal, I never feel like it's enough. And although I have a rich French and Irish heritage, because I don't look “white” I feel like I will never be enough to identify. So I guess the question I am left with is: will I ever be enough? And are myself and my friend the only multiracial people that feel this way?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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; both teacher heads agree on what is shown here.
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".