Bibliographic record
Abstract
Hadiksm Gaax di waayu, I belong to the Ganhada (Raven Clan) and my Mother’s side of the family is from Gitxaala, we follow our Mothers. This research, writing, and data collection was done on the traditional unceded territories of the Tsimshian, Lheidli T’enneh, and Musqueam. This work was done in partnership with the people who shared their stories with me, the co-researchers, whose words provide a brief glimpse into the lived experience of First Nations identity and the thought processes involved in contemplating several sources of input informing how we think about identity. Stories of identity, perceptions of identity, and experiences of racism and discrimination have inspired this work and highlighted the need for engagement. This research is a validation of thought processes that surround how we, First Nations people, experience identity. A shift away from Western conceptualizations of identity, this research discusses experiential knowledge, racism and discrimination, impacts of racial microaggressions on self-perceptions and health, and a sampling of how some people have come to define their identity in their own way based on their experiences. The intent of this work is to both inform those who may not understand and to acknowledge and validate those who have thought about First Nations identity but do not have a safe space to share. I hope this work speaks to both First Nations and non-First Nations/Settler Canadians as we continue learning about one another and sharing with each other in the spirit of reconciliation.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.066 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".