MétaCan
Menu
Back to cohort
Record W93368576

Through a Colored Looking Glass: A View of Judicial Partition, Family Land Loss, and Rule Setting

2000· article· en· W93368576 on OpenAlexfundno aff
Phyliss Craig-Taylor

Bibliographic record

VenueOpen Scholarship Institutional Repository (Washington University in St. Louis) · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
FundersHenan Institute of Science and TechnologyRoyal Canadian Geographical Society
KeywordsColoredPartition (number theory)LawPolitical scienceMathematicsCombinatorics
DOInot available

Abstract

fetched live from OpenAlex

African Americans in contemporary U.S. society continue to experience economic inequality. Regardless of the indicators one reviews—property ownership, employment, or income—the data confirm the entrenchment of African Americans’ disadvantaged status. It is the thesis of this Article that the economically subordinated status of African Americans cannot be divorced from the historical processes that have created or contributed to the divide between African Americans and other social groups. In this Article, I explore the struggle of ex-slaves and their twenty-first century descendants to achieve the promise of property in a democratic society and the role of law as a reflection of and tool of racial hierarchy. I focus on the role played by the judicial partition process in creating a system that inextricably led to stripping African Americans of their property. In recent years, courts and legislatures have scrutinized and expressed increasing hostility toward exercises of regulatory power that effect takings. This Article gives a similar scrutiny to the partition process and argues that it should be modified to alleviate the unfair burden it places on poor property owners and to assure that property owners are afforded just compensation when their property is involuntarily taken to achieve a public purpose.

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)0.000
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 score0.683
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.026
GPT teacher head0.279
Teacher spread0.253 · 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

Citations26
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueOpen Scholarship Institutional Repository (Washington University in St. Louis)Same topicJudicial and Constitutional StudiesFrench-language works237,207