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:
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".