The terrifying abyss of insignificance: Marginalisation, mattering and violence between young people
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".