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2022 Trafficking in Persons Report: Mexico

2022· article· en· W6987703878 sur OpenAlexaboutno aff

Notice bibliographique

RevueFlorida International University Digital Commons (Florida International University) · 2022
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSex work and related issues
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésExploitRefugeeIndigenousHuman traffickingPoison controlSuicide prevention
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

As reported over the past five years, human traffickers exploit domestic and foreign victims in Mexico, and traffickers exploit victims from Mexico abroad. Groups considered most at risk for trafficking in Mexico include unaccompanied children, Indigenous persons, persons with mental and physical disabilities, asylum seekers and migrants, IDPs, LGBTQI+ individuals, informal sector workers, and children in gang-controlled territories. Traffickers recruit and exploit Mexican women and children, and to a lesser extent men, in sex trafficking in Mexico and the United States through false promises of employment, deceptive romantic relationships, or extortion. The majority of trafficking cases occur among family, intimate partners, acquaintances on social media, or through employment-related traps. The online sexual exploitation of children reportedly increased during the year. Transgender persons are particularly vulnerable to sex trafficking. Traffickers increasingly use the internet, particularly social media, to target and recruit potential victims. Traffickers exploit Mexican adults and children in forced labor in agriculture, domestic service, child care, manufacturing, mining, food processing, construction, tourism, begging, and street vending in Mexico and the United States. Traffickers commonly exploit day laborers and their children in forced labor in Mexico’s agricultural sector, with most victims coming from economically vulnerable and Indigenous populations. Individuals migrate from the poorest states to the agricultural regions to harvest vegetables, coffee, sugar, and tobacco; many receive little or no pay or time off; endure inhumane housing conditions without access to adequate food, clean water, or medical care; and are denied education for children. Some employers withhold weekly wages to compel agricultural workers to meet certain harvest quotas or continue working until the end of the harvest. Recruiters frequently employ deceptive recruitment practices and charge unlawful fees to place agricultural workers in Mexico and the United States; many workers are promised decent wages and a good standard of living, then subsequently compelled into forced labor through debt bondage, threats of violence, and non-payment of wages. NGOs estimated traffickers increasingly exploited individuals in forced labor in Mexico. The vast majority of foreign victims of forced labor and sex trafficking in Mexico are from Central and South America, particularly El Salvador, Guatemala, Honduras, and Venezuela—with Venezuelan victims increasing in recent years; traffickers exploited some of these victims along Mexico’s southern border. NGOs and the media report victims from the Caribbean, Eastern Europe, Asia, and Africa have also been identified in Mexico, some en route to the United States. Among the Cuban medical professionals the government contracted to assist during the pandemic, some may have been forced to work by the Cuban government. Thousands of Ukrainian refugees, predominantly women and children who are fleeing Russia’s war on Ukraine, have arrived in northern Mexican border cities seeking sanctuary in the United States and are vulnerable to trafficking. Organized criminal groups profit from sex trafficking and force Mexican and foreign adults and children to engage in illicit activities, including as assassins, lookouts, and in the production, transportation, and sale of drugs. Experts expressed particular concern over the forced recruitment of Indigenous children by organized criminal groups, who use torture and credible threats of murder to exploit these children in forced criminality. Criminal groups exploit thousands of children in Mexico to serve as lookouts, carry out attacks on authorities and rival groups, or work in poppy fields. Observers also expressed concern over recruitment of recently deported Mexican nationals and foreign migrants by organized criminal groups for the purpose of forced criminality. Migrants and asylum seekers in or transiting Mexico are vulnerable to sex trafficking and forced labor, including by organized criminal groups; this risk is particularly high for migrants who rely on smugglers. Observers, including Mexican legislators, noted links between violence against women and girls and between women’s disappearances, murders, and trafficking by organized criminal groups. Observers reported potential trafficking cases in substance abuse rehabilitation centers, women’s shelters, and government institutions for people with disabilities, including by organized criminal groups and facility employees. Trafficking-related corruption remains a concern. Some government officials collude with traffickers or participate in trafficking crimes. Corrupt officials reportedly participate in sex trafficking, including running sex trafficking operations. Some immigration officials allegedly accept payment from traffickers to facilitate the irregular entry of foreign trafficking victims into Mexico. NGOs reported child sex tourism remains a problem and continues to expand, especially in tourist areas and in northern border cities. Parents are sometimes complicit in exploiting their children in child sex tourism, and children experiencing homelessness are believed to be at high risk. Many child sex tourists are from the United States, Canada, and Western Europe; Mexican men also purchase sex from child trafficking victims. Authorities reported trafficking networks increasingly used cryptocurrencies to launder proceeds from their crimes. Economic hardship resulting from the pandemic led some workers to accept loans from their employers that left them highly vulnerable to debt bondage.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,856
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0020,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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.

Tête enseignante Opus0,014
Tête enseignante GPT0,236
Écart entre enseignants0,221 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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