Kind of Blue: Lamenting the Failures of Settler Christianity in a Twilight Civilization
Bibliographic record
Abstract
Amid the Black Lives Matter Movement and the ongoing struggle for the recognition of Indigenous rights, and in the wake of police murders of racialized minorities and state-sponsored cultural genocide, oppressed and marginalized groups are calling settler Christians to reckon with the legacy of colonialism that continues to haunt their cherished religious traditions. As a settler Christian, I here share my first halting steps to undertaking such a reckoning. It is a story about finally starting to listen, getting unsettled, and staying there. I have also found it to be a lament of sorts— a journey plunging me into a kind of blue that is perhaps different than the blues sung by those who have directly suffered the poverty and violence of colonialism’s oppressive legacy. It is, finally, a lament over the destructive “twilight civilization” (to use Cornel West’s phrase) this legacy has wrought—an unsustainable civilization that in a variety of ways dehumanizes everyone, whether we share the identity of the oppressor, the victim, or a mixture of both. Perhaps lament, then, is the spiritual practice settler Christians must first take up as we strive to work through and work free from our damaged past. That is, perhaps this reckoning calls settler Christians to perform a certain leavetaking before we may truly join our hearts and voices with those who seek to keep faith with all their fellow humans as well as the rest of God’s good Creation.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.045 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.016 |
| 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".