The hummingbird & me: Experiences with shattering as a settler educator and emerging scholar
Bibliographic record
Abstract
I explore my experiences as a settler teacher and emerging scholar by unpacking time and place in the short children’s story The Little Hummingbird by Michael Nicoll Yahgaulanaas. I demonstrate my understanding with a hyperlapse video of traditional beading, a skill taught to me by local Indigenous Elders. I begin to unpack myself and others by centering myself as the hummingbird, the protagonist of the short story who continues to put drops of water on a raging forest fire, even though it will not put out the flames. In this retelling, I problematically view myself as a settler hero who is doing “good”, ignoring the ongoing nature of colonialism and the benefits I gain from the hierarchy of relations in Canada. In the second retelling, I become the fire destroying the forest. I recount the shattering of my settler-as-hero self-proclaimed identity, and begin to accept how I am complicit in colonial violence towards Indigenous peoples. By watching my hands work the pattern and beads, I physically depict the slow work necessary for arriving at the actualization of bigger possibilities for settler teachers and emerging scholars, like a hummingbird with a drop of water to douse fire.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".