Child Sexual Abuse: Toward a Conceptual Model and Definition
Bibliographic record
Abstract
The problem of defining "child sexual abuse" (CSA), and the need to define this concept, has been recognized by major policy bodies and leading researchers since the 1970s. Recent demands for a more theoretically robust, explicit definition of CSA show this challenge remains urgent. In this article, we identify problems caused by variance in definitions of CSA for five domains: research and knowledge formation, legal frameworks and principles, prevention efforts, policy responses, and the establishment of social norms. We review and analyze definitions used in leading international epidemiological studies, national and international policy documents, social science literature, and legal systems in the United States, Canada, and Australia to demonstrate the continuing use of different concepts of CSA and identify key areas of conceptual disagreement. Informed by our literature review, we use a methodology of conceptual analysis to develop a conceptual model of CSA. The purpose of this model is to propose a more robust, theoretically sound concept of CSA, which clarifies its defining characteristics and distinguishes it from other concepts. Finally, we provide operational examples of the conceptual model to indicate how it would translate to a classificatory framework of typologies of acts and experiences. A sound conceptual model and classificatory system offers the prospect of more appropriate and effective methods of research, response, regulation, and prevention. While total consensus is unattainable, this analysis may assist in developing understanding and advancing more coherent approaches to the conceptual foundation of CSA and its operationalization.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.004 | 0.010 |
| 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".