Innes: Security Bonds
Bibliographic record
Abstract
Risk in society is a pertinent concept of late modernity. Most elements of our social and interpersonal lives are in some way linked to concerns about safety, security and fear of harm. As a consequence, we spend a great deal of time engaged in emotional, physical and economic processes that facilitate our safety. Whether this be through purchasing anti-theft devices, or subscribing to self-defense training courses; participating in neighbourhood-watch schemes or altering our behavior to prevent susceptibility to victimization, all demonstrate an inherent pre-occupation with risk and perceived danger. The work presented in this paper offers an in-depth socio-criminological analysis focusing on the issue of citizens insecurity, and proposes an original interpretative paradigm emerging from findings on the INNES (Intimate Neighborhood Strengthening) European Project. A presentation of the idiographic and nomothetic motivations and conditions influencing and predicting social fears and insecurities over the last two decades is discussed, with the presentation of the new interpretative model, ‘Social Cobweb Theory’. This model focuses on solidarity and on the strengthening of intimate neighborhood bonds and argues that these aforementioned concepts function as an effective approach in lowering citizens' perceptions of individual insecurities and risk.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".