Lessons from insiders: Embracing subjectivity as objectivity in victimology
Bibliographic record
Abstract
Due to the prevalence of victimization in society, it is likely that many victimologists have been victimized or will be in their lifetimes. This poses a challenge for the field of victimology as traditional, positivist conceptions of 'good science' require researchers to be outsiders relative to populations they study. This paper asks: What are the epistemological and practical implications of victimological research conducted by researchers who have firsthand experiences of victimization? What lessons can be retained by other victimologists and researchers in general? How can these epistemological considerations be applied in practice? To answer these questions, I examine the meanings of insider and outsider status and the implications for objectivity and subjectivity as per positivist and standpoint epistemologies. I present the case of victimologists who have been victimized as well as the advantages and disadvantages of this form of insider research. I deconstruct insider-outsider, subjectivity-objectivity dualisms as they pertain to victimologists, concluding that all victimologists can be subjective whether they are technically insiders or not. In closing, I discuss how all victimologists can embrace their own and their participants' subjectivity as a resource for objectivity by examining location, emotions and bodies, and ethics throughout the research process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".