Traces Retraced: Reconstructing Identity
Bibliographic record
Abstract
“I am Métis/Anishinaabe” is a complicated statement to make in 2018. Determining the precise meaning behind the various terminology used to describe the Métis throughout Canadian history can be confusing – terms like Country-Born, Black-Scotts, Bois Brûlé and Half-breed, each possess their own distinction. I have resolved my use of the term Métis/Anishinaabe to self-identify, based on extensive genealogical, archival and qualitative research and through the personal relationships I’ve established since 1999. Recovering my Métis/Anishinaabe roots enabled me to develop a relationship with my Indigenous ancestral history and participate in the evolution of my culture today. By utilizing artifacts and stories of my own family’s history and interpreting the emergence of my identity through the processes of art making, I advance a methodology of coming to know. This thesis confronts contemporary issues around reconstructing identity and retracing the threads of my ancestry to recover what was left by the trail.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".