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Record W7084204959

Ways of Belonging:Undocumented Youth in the Shadow of Illegality

2023· book· en· W7084204959 on OpenAlexaboutno aff

Bibliographic record

VenueResearch Portal (King's College London) · 2023
Typebook
Languageen
FieldComputer Science
TopicEngineering and Information Technology
Canadian institutionsnot available
Fundersnot available
KeywordsInvisibilityShadow (psychology)ImpossibilityEthnographyAmbivalenceConversation
DOInot available

Abstract

fetched live from OpenAlex

Ways of Belonging examines the experiences of undocumented young people who are excluded from K-12 schools in Canada and are rendered invisible to the education system. The law doesn’t mention the existence of undocumented children, thus their access to education rests on discretionary practices and is often denied altogether. This book brings the stories of undocumented young people vividly alive, putting them into conversation with the perspectives of the different actors in schools and courts who fail to include these young people. Drawing on five years of ethnographic fieldwork, Francesca Meloni shows how ambivalence shapes the lives of young people who are caught between the desire to belong and the impossibility of fully belonging. Meloni pays close attention to young people’s struggles and hopes, showing us what it means to belong and to endure in contexts of social exclusion. Ways of Belonging reveals the opacities and failures of a system that excludes children from education and puts their lives in invisibility mode.

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)0.002
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 score0.107
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0220.016
Scholarly communication0.0090.006
Open science0.0010.006
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.057
GPT teacher head0.305
Teacher spread0.247 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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