Like a Rolling Stone: Confronting Privilege and Power in the Fight Against Trafficking
Notice bibliographique
Résumé
“Ahh you’ve gone to the finest schools, alright Miss Lonely, But you know you only used to get juiced in it. Nobody’s ever taught you how to live out on the street, and now you’re gonna have to get used to it. How does it feel, how does it feel? To be on your own, with no direction home. Like a complete unknown, like a rolling stone” (Dylan, 1965). Let us be clear. Far too many people are trafficked and far too few traffickers or buyers are caught, found guilty, or punished. An estimated 27 million people live in modern slavery worldwide, with 12 million of them being children (U.S. Department of State, 2024). In the United States (U.S.), sex trafficking is the most common type of trafficking (U.S. Department of State, 2024), disproportionately involving Black girls (Congressional Black Caucus Foundation, 2020). Since 2019, between 202 and 289 federal human trafficking cases were filed annually. Nearly all of the 2023 cases (98%) filed were sex trafficking (versus forced labor trafficking; caveat: the feds tend to focus more heavily on sex trafficking); the cases involved 638 victims aged 4–21 (average age 15) who were predominantly female and U.S. citizens, and had been recruited via social media tools including Snapchat and Facebook (Human Trafficking Institute, 2024a; U.S. Department of State, 2024). Despite a 96% conviction rate, a mere 10 traffickers were convicted to life sentences in 2023 (Human Trafficking Institute, 2024b). With so many children and women trafficked and so few networks, traffickers, and sex buyers stopped [see long overdue recent court cases involving Canadian billionaire Robert Miller (Banerjee, 2025) and American Michael Jeffries, former Abercrombie & Fitch CEO (U.S. Department of Justice, 2024)], every sense of danger, injustice, disgust, and intolerance should be heightened. The JFN editorial board commissioned this special issue on trafficking with earnest pursuit of solutions and intentional avoidance of restating the problem. You will read compelling and inspiring articles offering strategies, tools, and cautions which will strengthen your forensic nursing care for the trafficked. Absorb these shared best practices, test them in your settings, communicate back your lessons learned, and contribute to producing tangible tactics that work. In August 2025, the International Association of Forensic Nurses annual conference will convene in Omaha, Nebraska. As you prepare to learn, network, and grow, consider taking a deep and dark dive into how one national child-trafficking network was disrupted when the Franklin Federal Credit Union was investigated (Nickel, 2012). While we forensic nurses fight sex trafficking via our direct care for those who survive, we must do so with eyes wide open, recognizing the complex, historic, and present-day webs of money, power, politics, and addictions in which trafficking occurs. And when we do let ourselves see what can’t be unseen, rather than be lost in discouragement or a sense of powerlessness, we should stand resolute in doing what is in our control, just as American musician Bob Dylan did in using his lyrics to fight injustice (Hurricane), protest war (Masters of War), challenge privilege (Like a Rolling Stone), and many more. Like Bob Dylan hauntingly describes, today we might be the privileged, the ones untouched by the tangible pain of losing a loved one to human trafficking, of someone missing or murdered. On another day, we might be the ones grieving a lost loved one, or experiencing the pain of their unknown status. We write this editorial with determination to never be those privileged people who, in day-to-day living, ignore or miss signs of trafficking. And while we try not to overindulge our hypervigilance, a symptom of secondary traumas from being forensic nurses, we should, no, we must, lean into the heightened awareness and intuition our hypervigilance fuels to recognize harms or threats in our proximities, and to help others do the same in theirs. This means, for example and for starters, we can’t get lost in our own priorities, letting our lists of to-dos draw attention away from what might be happening around us, locally and globally. We, like you, and like all who, at times, hold positions of care, of authority, of privilege, and of freedom, must live our intolerance of trafficking and of injustices with explicit antitrafficking, bold, and defiant hearts, minds, voices, and actions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».