Bibliographic record
Abstract
Let's acknowledge at the outset that Gloria is fictional. But there are real-life Glorias even in Vermont, a state known for its picturesque villages, ski slopes, and green pastures. They are abuse victims whose husbands or boyfriends are serving time in jail. In this scenario, says Dr. Thomas Powell, director of clinical services with the Vermont Department of Corrections, let's place Gloria's husband at the Northern State Correctional Facility, one of Vermont's highest-security prisons, within hailing distance of the Canadian border in Vermont's rugged Northeast Kingdom. Traveling to see him on visitors' day is an arduous proposition. So what do we do when she gets there, with her children in tow? asks Powell. We greet her with security apparatus and security officials who are large males, and who make her go through metal detectors and contraband-detection procedures. If this is a woman with a history of traumatic abuse, she feels threatened, intimidated, and controlled from the moment she enters our facility. The same holds for her children, who may have been abused, or have witnessed abuse, themselves. But the Glorias and their children aren't the only ones affected by trauma in Vermont. Trauma appears across the spectrum of the human services client population. A legislative Commission on Psychological Trauma reported in 2000 that perhaps 70 percent of people in Vermont's outpatient mental health treatment programs, and up to 72 percent in inpatient facilities, had traumatic abuse histories. Seventy percent of the participants in substance abuse treatment programs experienced physical or sexual abuse. The trend holds even for PATH (the Department of Prevention, Assistance, Transition and Health Access, formerly Social Welfare). one primary reason they end up in (need of) our agency's says Susan Besio, commissioner of Vermont's Developmental and Mental Health Services. To address the high incidence of trauma, in April 2001, Jane Kitchel, Vermont's then secretary of human services, designated trauma as the Agency of Human Services' (AHS) seventh, and most recent, cluster. Kitchel appointed Besio cochair of a cross-departmental working group. In September 2002 the cluster hosted a three-day, agency-wide training with nationally recognized trauma experts Maxine Harris and Roger D. Fallot. The objective was to make the disparate departments operating under AHS' wide umbrella trauma-informed, modifying procedures to lessen the chances of alienating people with trauma histories, and to become more knowledgeable about referrals for treatment. It's an important step because long-term trauma, with its pernicious emotional consequences, can subvert the best efforts of human services providers. Once we start screening effectively for trauma throughout the agency, it might be the cross-cutting issue that allows us an understanding of families' systems and provides new perspectives on how to treat them, says Powell. Agency administrators have similar ambitions for all seven policy clusters (though the cluster concept itself is fluid, so more clusters could be created while existing clusters tail off as their work is completed or morph into some variation on their original theme.) Besides Trauma, there are clusters for Integrated Service Delivery, the High-Risk Pool, Coordinating the Caregiver Process, Connecting Better with Communities, Crisis and Family Stabilization, and a Coordinated Approach to Home Visits. Adaptable and Fluid The goal of transforming not just a department or two, but the entire agency; into a trauma-informed social services organization suggests the potential dimensions of Vermont's policy cluster concept. If AHS tried to address trauma in the pre-policy cluster mode, one might almost imagine the creation of a Department of Trauma, replete with bureaucracy, budget, and reporting requirements. …
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.048 | 0.014 |
| Scholarly communication | 0.027 | 0.023 |
| Open science | 0.009 | 0.038 |
| Research integrity | 0.033 | 0.023 |
| Insufficient payload (model declined to judge) | 0.045 | 0.003 |
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".