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Record W2795273097 · doi:10.1080/0966369x.2018.1443909

A century of Grace; restorative spatial justice, pedagogy, and beloved community in twenty-first century Detroit

2018· article· en· W2795273097 on OpenAlexafffund
Rachael Baker

Bibliographic record

VenueGender Place & Culture · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsYork University
FundersYork UniversityFulbright Canada
KeywordsBiographySociologyDialecticEconomic JusticeSpace (punctuation)Gender studiesEpistemologyLawPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

This article poses feminist biographical investigation as a dialectical approach to situated knowledge, and as a potential avenue for a feminist theorization of space and place. By exploring biography as a departure from canonical epistemological structures, the attempt here is to credit, contextualize and identify key places and people of origin in the evolution and production of theory and knowledge without such heavy dependency on the usual resources that legitimize theoretical and pedagogical contributions; such as academic publications, teaching contributions and references. The biographical focus of this article is the life and work of Grace Lee Boggs, an important contributor to urban studies whose theoretical and pedagogical contributions have gone largely unacknowledged by geographers and spatial thinkers. What can a biographical investigation teach us about feminist knowledge production relating to the production of space? What does feminist biography offer epistemologically to our understandings of space? These questions are examined here through the theoretical contributions of Grace Lee Boggs, a long-time resident of Detroit, second generation Chinese American, civil rights and feminist activist and working class philosopher, as a means of exploring biography as a feminist research methodology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.318
Teacher spread0.283 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2018
Admission routes2
Has abstractyes

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