Bibliographic record
Abstract
In July 2020, I relocated from the territory of the Lenape in New York City, New York, to the ancestral homelands of the Coast Salish Peoples and the Lummi Nation and Nooksack Tribe, otherwise known as Bellingham, Washington. As a settler Canadian and "dependent" on my partner's US work visa, I wrestle with my precarious yet privileged footing here in the southern part of Turtle Island. As well, friends and family often ask me how I am "settling in." I deploy this very question as a provocation to ask, As a white settler, what does it mean to both responsibly unsettle oneself and "settle in" to a new home on stolen land? At the same time, due to the complexities of moving across the country during COVID-19, I feel unmoored and disconnected from my immediate surroundings. I am the most grounded when I am dancing. Working through the metatarsals of my feet, those bones that absorb shock and engender soft landings, is both a metaphor and a methodology for my practice-based research as a settler artist-scholar. Thus, through a piece that is part photo essay and part embodied reflection, I move with the land here on the west coast. With Whatcom Falls Park as my studio and soundscape, I will work through these questions and acknowledge the Coast Salish Peoples, the Lummi Nation, and the Nooksack Tribe, on whose land I currently move.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".