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Record W7055411995

Citizens of Tomorrow, Today: Political Futurity and Youth Citizenship in a High School Student Council

2019· other· en· W7055411995 on OpenAlexaboutno aff

Bibliographic record

VenueYorkSpace (York University) · 2019
Typeother
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
Fundersnot available
KeywordsPretextCircumstantial evidenceNucleofectionGestational periodTSG101Hyporeflexia
DOInot available

Abstract

fetched live from OpenAlex

Student councils are ubiquitous in North American high schools, and yet, they are often overlooked as a site of youth citizenship and student leadership. This dissertation explores the affective dimensions of youth citizenship and student leadership, focusing on a student council in an urban high school in Canada. Based on a 15-month ethnography, the dissertation claims that affective orientations toward politics and young people are often at odds with the developmental and relational realities of being a young person. Through analysis of participant observations, interview transcripts, and texts, the study draws attention to the way that the relationships that structure schooling circumscribe young peoples citizenship and leadership. Drawing on theories of affect and political theory, the study focuses on three areas toward which the young people in the studys political lives were oriented: infantile citizenship (Berlant, 1997), authority and political conflict, and happy diversity (Ahmed, 2012). The dissertation concludes with a call for educators, policy-makers, and scholars interested in childhood and youth to consider the political and affective conditions that propel impulses to improve upon young peoples enactment of citizenship in terms of the attachment to the promise of young peoples political futurity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.743

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.006
Scholarly communication0.0050.001
Open science0.0000.003
Research integrity0.0010.002
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 head0.028
GPT teacher head0.207
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes1
Has abstractyes

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