Bibliographic record
Abstract
There is a small but growing practice of music therapy with women survivors of violence, first identified and described in 1990 by Cassity and Theobod and gradually growing since then (Austin, 2006; Curtis, 2000, 2006, 2007, & 2008; Curtis & Harrison, 2006; Day, Baker, & Darlington, 2009; Fesler, 2007; Gonsalves, 2007; Hahna & Borling, 2004; Hammel-Gormley, 1995; Hernanadez-Ruiz, 2005; Lasswell, 2001; MacIntosh, 2003; Montello, 1999; Rinkler, 1991; Rogers, 1993 & 1994; Slotoroof, 1994; Teague, Hahna, & McKinney, 2006; Ventre, 1994; Whipple & Lindsey, 1999; York, 2006). This has accompanied an increasing awareness overall of the serious extent and nature of violence against women. In the United States, during their lifetime, one in four women will experience domestic violence and one in five women will be raped, with 1.3 million women raped every year and an average of three women per day killed by their intimate partners (Black et al., 2011; Kanani, 2012). Furthermore, an estimated 12 to 38% of American women have experienced childhood abuse (Schacter, Stalker, & Teram, 2001). Yet it is difficult to accurately document the full prevalence of violence against women because of underreporting and undercounting (Curtis, 2006; Hahna & Borling, 2004; Kanani, 2012). These are hidden crimes with many reluctant to report because of the personal nature of the violence and for reasons of fear and shame. With gender frequently neglected in reporting processes, the challenge to fully capture the incidence rate is further exacerbated. While violence against women has been ignored or overlooked until recently, there is now a growing recognition that it is pervasive, persistent, and incredibly detrimental. This recognition includes an understanding of the broader scope of the costs of such violence—the personal costs (both short-term and long-term) and the societal costs in terms of public health, criminal justice, and the economy (Curtis, 2008; Kanani, 2012; Statistics Canada, 2006). At the societal level, economic costs alone for women, children, and communities run in the billions of dollars annually. These include medical and mental health care costs, law and legal services costs, shelter and foster care costs, property loss, and work place costs such as productivity loss (Teague, Hahna, & McKinney, 2006). At the personal level, the cost is immeasurable (Curtis, 2007; Kanani, 2012). Ultimately, it damages the very fabric of social justice. In the estimation of Susan Carbon, Director of the U.S. Department of Justice Office of Violence against Women:
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".