Bibliographic record
Abstract
Victims have always been a part of the criminalization process. In recent times the introduction of victimology and the victim’s rights movement has influenced the importance and awareness of victim’s roles and treatment within legislation and society. The identity of a victim, however, is not natural. It is socially constructed. In this paper I employ a critical victimological lens to examine how victims of crime have been seen and treated historically. The historical analysis focuses on the late 19th and 20th century when the views of victims began to dramatically shift. By analyzing this historical social construction one can see more clearly how this identity is always changing. We can see how this applied label affects not only how victims see themselves but how the perception of other individuals and current legislation affects them as well. I argue that this historical understanding is necessary to make sense of contemporary policies and treatment. In this essay I will answer how the identity of victim constructed and continuously being constructed. Discipline: Sociology Honours Faculty Mentor: Dr. Amanda Nelund
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 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".