Notice bibliographique
Résumé
Most Americans do not know that victims of trafficking are right here, suffering in the dark. Trafficking is practiced in many forms and in places you'd least expect. The simple truth is, humans keep slaves; we always have. This is capitalism at its worst. Before the Civil War, slaves cost a lot. In the 1850s, a slave sold for around $1,200. In today's currency, that comes to somewhere between $40,000 and $50,000. This level of investment predisposed the owners to take care of their human property, at least to the extent that their longevity and their productivity were ensured. Today's slave can be bought for as little as $100. This price tag makes the modern slave not only affordable, but also disposable. Further, trafficking comes with a bundle of other crimes, including kidnapping, document fraud, assault, torture, rape and sometimes homicide. According to a U.S. State Department study, some 17,000 foreign nationals are trafficked into the United States from at least 35 countries and enslaved each year. Some victims are smuggled into the U. S. across the Mexican or Canadian borders; others arrive at our major airports daily, carrying either real or forged papers. Victims from Africa, Asia, India, Latin America and the former Soviet Union come on the promise of a better life, with an opportunity to work and prosper in America. Many arrive in the hope of earning enough money to support or send for their families. In order to pay for the journey, they use their life savings, or go into massive debt to people who will take advantage of them. Instead of opportunity, they find bondage. They can be found — or more accurately, not found — in all 50 states, working as farmhands, domestics, sweatshop and factory laborers, gardeners, workers in the restaurant, construction and sex industries. These people are not poorly paid employees, working at jobs they might not like. They are workers who are unable to leave and forced to live under the constant threat of violence. Although today's term may be human trafficking, by both historical and legal definition, these people are slaves. What is particularly infuriating is the fact that the crime of trafficking almost always goes unpunished. When the U.S. government and the media address the subject of human trafficking, they tend to focus on sexual exploitation, whose victims are subjected to serial rape, physical injury, psychological damage, and constant exposure to sexually transmitted diseases. Most of the less sensational forms of slave labor are right under our noses. Domestics and nannies account for a significant number of America's slaves. Agriculture is another major area of human trafficking. There are unknown numbers of victims of forced labor growing and picking our fruit and vegetables. They come here looking for steady work and a decent wage. Instead, they are enslaved by crime syndicates, families or individuals in such states as Colorado, New York, North and South Carolina, Georgia, California and Florida.... Keywords: Human trafficking Language: en
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,008 |
| Communication savante | 0,010 | 0,011 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,039 | 0,014 |
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 source (Gemma direct ou Codex distillé), 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 ».