Bibliographic record
Abstract
As a newcomer to Canadian culture, I present an interpretive rendering of my encounters with settler and Indigenous relations. It is my humble attempt to respond to the Truth and Reconciliation Calls to Action ([TRC], 2015) for newcomers, by providing insight into what newcomers might experience in response to the complexities of Indigenous and settler dialogues. Newcomers are diverse groups, on the fringes of Indigenous-settler relations discourse, and outside of the protocols to enter such dialogues. Therefore, I ask, where and when can newcomers, temporary or long term, enter the dialogues in meaningful, respectful ways? I came to recognize that as a newcomer the more appropriate course of action would be to wait to be invited into the conversation; but that does not absolve me of the responsibility to inform myself about Indigenous-settler relations and confront my discomforts with how I am implicated in these relations. This led me to inquire, can newcomers be of value in the ways multiple ethnic groups live together, in a good way? Using a hermeneutic and mythopoetic lens I present a series of vignettes that attempt to grapple with these questions, to contribute to the discourse of responses to the Calls to Action (TRC, 2015).
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.056 | 0.056 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 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".