Bibliographic record
Abstract
People who go missing are often perceived to have done so voluntarily, and yet, many missing persons in Canada are Aboriginal, visible minorities, homeless, and are fleeing from violence, abuse, and neglect. Integrating the concept of social exclusion and an intersectional perspective with a sample of 724 missing persons cases drawn from one Canadian police service, this dissertation examines the systemic issues underlying peoples’ disappearances. This dissertation also explores the role of social and economic disadvantage in the risk of a long term disappearance. A combination of univariate (descriptions), bivariate (cross-tabulations), and multivariate (logistic regression) analyses identify correlates and causes of going missing and correlates and causes of long term disappearances. \nThe concept of social exclusion explains how structural processes prevent particular groups and individuals from gaining access to valued social relationships and economic opportunities in a particular society, resulting in considerable hardship and disadvantage. This dissertation argues that people who are marginalized and excluded have few resources to rely on to cope with stress and strain and may resort to going missing if confronted with adversity. Groups who are overrepresented among missing persons compared to the general population are identified by cross-tabulations and chi-square tests. Multivariate analysis (partial tables and logistic regression) is used to control for possible sources of spuriousness, in order to have more confidence in imputing causal relationships between membership in disadvantaged groups and going missing. \nMoreover, if disadvantaged groups go missing, they further sever ties with families, the labour market, and other mainstream institutions. As a result of extreme disadvantage, they may find it difficult to (re)connect with conventional social relationships and mainstream society. For example, youth who are escaping violence and abuse at home often end up on the streets and sever ties with schools, families, and other conventional support networks and become engaged in street culture. As a result of extreme disadvantage these young people are at risk of a long term disappearance. In other words, social exclusion is expected to be a risk and causal factor in long term disappearances. Groups who are overrepresented among long term disappearances compared to short term disappearances are identified by cross-tabulations and chi-square tests. Logistic regression analysis is used to draw conclusions about causal factors in long term disappearances. \nThis research finds that excluded groups such as disadvantaged youth, Aboriginal people, women and other visible minorities, victims of violence, and youth in care are at disproportionate risk of going missing. Consistent with an intersectional perspective, this dissertation shows that certain groups who are multiply marginalized such as Aboriginal women and young women face an especially high risk of going missing. Aboriginal identity, labour force status, and homelessness are also implicated as causal factors in peoples’ disappearances. Moreover, this research finds that social exclusion is a risk and causal factor in long term disappearances as Aboriginal people, homeless people, minorities and other excluded groups face a high risk of a long term disappearance. Linking missing persons with the concept of social exclusion highlights the role of structural issues in peoples’ disappearances and refutes the common misperception that going missing is a choice. In terms of policy, the findings from this research indicate that prevention and intervention depend on targeting poverty, discrimination, gender inequality, violence, and other structural issues associated with social exclusion.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".