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Record W2943659232 · doi:10.32316/hse/rhe.v30i2.4642

Rivka Feldhay and F. Jamil Ragep, eds., Before Copernicus: The Cultures and Contexts of Scientific Learning in the Fifteenth Century

2018· article· en· W2943659232 on OpenAlexvenueno aff
Margaret Gaida

Bibliographic record

VenueHistorical Studies in Education / Revue d histoire de l éducation · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsFifteenthCopernicusHumanitiesArtClassicsAstrobiologyPhysics

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.312
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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