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Enregistrement W6887730150 · doi:10.17605/osf.io/2dvcg

Youth radicalization in seven countries: The role of perceived inequality, political social media use, conspiracy beliefs, and parental involvement.

2024· other· en· W6887730150 sur OpenAlexaboutno aff

Notice bibliographique

RevueOpen Science Framework · 2024
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésRadicalizationContext (archaeology)PoliticsIdentity (music)Youth studiesInequalityTerrorismSocial identity theory

Résumé

récupéré en direct d'OpenAlex

Radicalization in youth has been identified as a global problem (Campelo et al., 2018; Kutiyski et al., 2021). This age group is considered particularly at risk due to their search for identity and sense of belonging, which extremist groups can readily tap into (Adam-Troian et al., 2021; Schröder et al., 2022). Despite young people’s vulnerability and the changing societal landscape in which (online) communication is no longer contained within country borders, research to date has often focused on adults in countries such as the United States and Germany (Emmelkamp et al., 2020; Wolfowicz et al., 2021; Zych & Nasaescu, 2022). To prevent youth radicalization in a global context, it is important to identify which factors are relevant to youth in the context of radicalization and to study to what extent these findings can be generalized to other countries. This will help identify both local and global risk and protective factors in youth, which is crucial to ensure positive youth development across different societies. Therefore, four predictors of radicalization that are expected to be relevant to youth in particular, will be studied internationally: perceived inequality, political social media use, conspiracy beliefs, and parental involvement. The aim of the current study is to examine these predictors of radicalization in seven countries: Austria, Canada, Czechia, Germany, the Netherlands, Poland, and Slovenia. First, we will study perceived inequality, as youth today are growing up in a world with increasing wealth inequality which influences their perspectives on what a “fair” distribution looks like (Goya-Tocchetto & Payne, 2022; Zucman, 2019). It is argued that increased perceived inequality can fuel radical ideas and behaviors as individuals try to correct perceived unfairness (van den Bos, 2020). Previous literature supports this argument as perceived inequality has been identified as a risk factor for radicalization, though mainly in adult samples (Franc & Pavlović, 2023). As the world wide web knows no borders, it is crucial to determine the global influence of social media, specifically political social media use. We will study political social media use, as young people are not only increasingly using social media for recreational purposes, but a large part of their civic engagement also takes place online (Chryssochoou & Barrett, 2017; Larson et al., 2019; Pandya & Lodha, 2021). Social media provides an easy platform for sharing opinions on social and political issues that make this possible (Chryssochoou & Barrett, 2017). As these platforms use algorithms to recommend content that is in line with users’ (political) preferences, political social media use can lead to reinforcement and polarization of opinions (Pariser, 2011). In other words, the more people discuss politics online, the more their opinions are reinforced through social media algorithms, and the more extreme their political opinions might become. In some cases, these extreme and polarized opinions can provoke and increase an individual's likelihood of accepting defensive violence (Andersen, 2023). In line with this, elevated levels of political engagement on social media indeed correlated with the presence of radical ideologies (Pedersen et al., 2017). Furthermore, engaging in political conversations with peers and consuming political content from the media had a more significant impact on young adults' inclination to participate in radical ways (Chui et al., 2022). Social media also plays a role in the spread of conspiracy beliefs, another factor that has been linked to radicalization in adults (Imhoff et al., 2021; Jolley & Paterson, 2020; Rottweiler & Gill, 2022). This relationship might be even more pronounced in young people as they are more likely to believe in conspiracy theories than adults (Freeman et al., 2022) and are more likely to be exposed to them as conspiracy theories are widespread online (Cinelli et al., 2022). Conspiracy theories are thought to be ‘radicalizing multipliers’ (Bartlett & Miller, 2010). By identifying a source of discontent and attributing it to a perceived wrongdoer, conspiracy beliefs allow for the potential to provide people with a narrative to channel their resentful feelings onto a target (Vegetti & Littvay, 2022). Finally, family dynamics play an important role in the lives of young people. Parental involvement, meaning the extent to which parents are involved and present in their children’s lives, is thought to be a protective factor of radicalization (Radicalisation Awareness Network, 2017; Zych & Nasaescu, 2022). It is theorized that parental involvement acts as a buffer against other risk factors or reduces the likelihood of children engaging in risk behaviors (Yu et al., 2023). However, to date, relatively little research has been conducted on parental protective factors of youth radicalization (Zych & Nasaescu, 2022). In sum, the aim of the current study is to examine the role of perceived inequality, political social media use, conspiracy beliefs, and parental involvement in youth radicalization and to test to what extent these findings can be generalized to youth in different countries.

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)0,003
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,746
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,006
Communication savante0,0010,001
Science ouverte0,0030,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,051
Tête enseignante GPT0,347
Écart entre enseignants0,296 · 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'étudeThéorique ou conceptuel
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
Publié2024
Routes d'admission1
Résumé présentoui

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