Rivka Feldhay and F. Jamil Ragep, eds., Before Copernicus: The Cultures and Contexts of Scientific Learning in the Fifteenth Century
Bibliographic record
Abstract
attention -even if it meant zooming out from the school more frequently -to why these decisions were made, to more fully understand why as well as how the school changed as it did.Grundy identifies as a desegregation advocate.She came to the story of West Charlotte seeking to capture the school's special magic in the 1970s and 80s.She offers many compelling points of evidence for how students learned from desegregated educational spaces -as well as of the work involved in building and sustaining these spaces.The segregation inside the school along academic tracks, or the persistent worry that, as one black parent put it, via desegregation "our people" would "be consumed by the white people" (55), reflect harder realities of the process of desegregation and perhaps could offer sources of insight for why the period of desegregated success proved short-lived.Grundy clearly acknowledges these difficulties and inequalities in the process of desegregation, but could plumb their origins and consequences to a greater extent.New approaches to desegregation today -those imagining explicitly anti-racist desegregation -have to face this complex history.One West Charlotte alumnus's view of desegregation in the 1970s has lessons for the present: "Our society is very witty."He continued, "and as new demands come upon us for changing we find new ways to entrench ourselves in the old" (114).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".