Reading the larger lessons of Sherman Alexie's literary rise and fall
Bibliographic record
Abstract
THIS WEEK / 'Sorry' for the racism: As National Geographic tries to atone for its problematic history with non-white people, we assess how much credit (and critique) they deserve. / 'Sorry' for the sexual harassment: As Native American writer Sherman Alexie continues his free-fall amid accusations of mistreating women, well read into his story for larger lessons. / 'Sorry' (not sorry) for the journalism: A Canadian reporter faces potential jail-time for embedding himself inside an Indigenous-led protest against an east coast mega-project. Joining host Rick Harp at this weeks roundtable are Kim TallBear, associate professor of Native Studies at the University of Alberta, and Candis Callison, associate professor at UBC's Graduate School of Journalism. // Our theme is 'nesting' by birocratic.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.164 | 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; both teacher heads agree on what is shown here.
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".