MétaCan
Menu
Back to cohort
Record W4286942542

The terrifying abyss of insignificance: Marginalisation, mattering and violence between young people

2021· article· en· W4286942542 on OpenAlexaff
Luke Billingham, Keir Irwin‐Rogers

Bibliographic record

VenueOpen Research Online (The Open University) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsInsignificancePsychologyCriminologySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

The concept of mattering can be a helpful for understanding the ways in which structural and historical factors affect individual psychologies. There is substantial evidence to indicate both that mattering is a fundamental human need, and that its lack can have very significant consequences, including the perpetration of physical harm against the self and others. This paper lays out the usefulness of mattering as a lens through which to examine why a small minority of young people in Britain commit serious violent acts against one another. We do this by first exploring what it means to matter and the empirical evidence linking the quest to matter with violence, and then examining the various factors in contemporary Britain which can diminish a young person’s sense of mattering, using recent community-based research which has amplified the perspectives of young people in London. We then contrast the insights that can be gained from the lens of mattering with some recent attempts on the part of the British government to effectively tackle violence between young people, including Knife Crime Prevention Orders and the curtailment of individuals’ social media usage. We conclude by suggesting that policy-makers would gain substantial insight from investigating the connections between marginalisation, mattering and violence between young people, rather than focusing disproportionately on the music they choose to listen to or create, or the specific weapon that they opt to carry.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0020.002
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 head0.167
GPT teacher head0.438
Teacher spread0.271 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueOpen Research Online (The Open University)Same topicYouth Education and Societal DynamicsFrench-language works237,207