Learning About Oneself: An Essential Process to Confront Social Media Propaganda Against the Resettlement of Syrian Refugees
Notice bibliographique
Résumé
Research Problem: \nPublic reaction to the 2015-2016 resettlement of Syrian refugees to Canada ranged from strong support to active resentment. This study explored some of those reactions: those of host society youth. It examined the process of this youth learning about themselves in the context of the social media propaganda about the resettlement of Syrian refugees, and investigated how the public opinion about the refugee resettlement affected their perception of their roles in the integration and inclusion of these newcomers. \nResearch questions: \n1.How do youth construe online interactions about the Syrian refugee crisis? \n2.How do youth construe their role in the integration and the inclusion of refugees in a context where the image of refugees is deeply influenced by social media? \n3.What knowledge and skills do youth develop when they engage in analyzing their thoughts and behaviours in regards to sensitive and controversial issues such as the refugee crisis and resettlement? \n4.How could this knowledge and these skills facilitate their engagement in civic online reasoning and participatory politics? \nMethodology: \nThe researcher conducted more than 160 hours of qualitative in-depth interviews with 42 host society youth between 18 and 24 years old from North America, Europe and the Middle East. For the purpose of this thesis, only data collected from the Canadian participants was analyzed and shared. The participants were recruited through a snowball sampling. They were active on social media, supportive of the Syrian refugee resettlement in Canada, but deliberately acting as passive bystanders whenever they encountered online posts and interactions about the Syrian refugee crisis. Adapting four techniques from George Kelly’s Personal Construct Psychology (Kelly’s self-characterization technique, Procter’s Perceiver Element Grid, Kelly’s Repertory Grid Test and Hinkle’s laddering technique), data collection included three to four interviews with each participant. The interviews provided the participants with opportunities to delve into their own construct systems and to reflect on the genesis of their constructs. \nResults and Conclusions: \nBy reflecting on their own behaviours online, participants realized that they could control how social media influenced them, and shape the online image of the Syrian refugees in host countries. While their empathy towards refugees increased, participants identified factors that could lead to Islamophobia, racism and fear, and developed strategies to counterbalance them online. The process of learning about themselves was key to transform the participants from passive bystanders into active agents of change, ready to confront digital propaganda. \nCivic educators, social workers, curriculum developers, policy makers and parents concerned with the takeover of social media by hate speech proponents can apply these findings and help youth withstand manipulation and fight racism, hate speech, radicalization, and cyberbullying through the Get Ready to Act Against Social Media Propaganda model generated by this study. The model includes five iterative stages: Question, analyze, design, prepare and evaluate.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».