MétaCan
Menu
Back to cohort
Record 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 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsRadicalizationContext (archaeology)PoliticsIdentity (music)Youth studiesInequalityTerrorismSocial identity theory

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.006
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.347
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueOpen Science FrameworkFrench-language works237,207