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Record W2293119428 · doi:10.1057/9781137032058_4

Learning by Dispossession: Objective Violence and Educational Failure

2013· book-chapter· en· W2293119428 on OpenAlexaboutno aff
Alexander J. Means

Bibliographic record

VenuePalgrave Macmillan US eBooks · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsDowntownShot (pellet)PovertyVisual artsQuarter (Canadian coin)CriminologySociologyPsychologyMedia studiesHistoryArtPolitical scienceLawArchaeology

Abstract

fetched live from OpenAlex

Ellison Square and CHS are located several miles south of the landmark buildings and lakeshore attractions of Chicago's downtown. The neighborhood and school are marked by concentrated poverty and ethnoracial segregation. They also contend with persistent issues related to violence. Indeed, when I first arrived in Ellison Square during the first week of September 2010 to begin this research, I found a school and community under emergency conditions because of two recent shootings involving students from CHS. One of these shootings took place on school grounds in full view of students, police, teachers, and parents, the other in a vacant lot near the school. Neither incident was fatal, both were gang related. Maya (African American freshman): I was in my class. I was in my division. I was right there because we were looking through the window. It was hot so we had opened the windows and we were looking out the window and we just saw the boy had just got shot and he was just lying there and somebody was like, "Get help." And that's when the teacher told us to sit down. All I saw was a car pulled over and the boy was just walking and they shot him. And that's when the teacher was like, "Sit down, stop instigating" and stuff like that…I just felt hurt. Because it was like, it's probably because of the gangs and the bad decisions he chose and stuff like that.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.608
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.263
Teacher spread0.252 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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