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Record W3176434504 · doi:10.3390/socsci10070241

Vital Conjunctures in Compound Crises: Conceptualising Young People’s Education Trajectories in Protracted Displacement in Jordan and Lebanon

2021· article· en· W3176434504 on OpenAlexfundno aff
Zoë Jordan, Cathrine Brun

Bibliographic record

VenueSocial Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
FundersEconomic and Social Research CouncilInternational Development Research CentreGlobal Challenges Research Fund
KeywordsTemporalitiesContext (archaeology)Futures contractDisplacement (psychology)SociologyValue (mathematics)Lived experienceGender studiesPsychologyHistoryPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This paper emerges out of a study with 293 young people (Syrians, Palestinians and nationals) living in contexts of compound crises and protracted displacement in Jordan and Lebanon. In the paper, we discuss how young people’s education trajectories can be conceptualised, operationalised and studied. We synthesise different approaches to understanding and analysing such trajectories into a framework that captures the intricate and multi-directional ways that young people navigate towards uncertain futures. The framework on multi-directional trajectories takes its starting point from an understanding of Victoria Browne’s ‘lived time’, captured through how different temporalities come together in one person’s story. After presenting our framework and the context in the first part of the paper, the second part applies the framework to analyse the ‘vital conjuncture’ of leaving education. By analysing leaving education as lived time, we create nuanced insights into how this vital conjuncture can be understood to shape young peoples’ trajectories. In conclusion, we discuss the value of understanding trajectories as lived time by illuminating how young people experience and navigate their education trajectories.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.977

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 studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.029
GPT teacher head0.373
Teacher spread0.345 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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