It's too late – the post has gone viral already: a novel methodological stance to explore K-12 teachers' lived experiences of adult cyber abuse
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
Purpose The purpose of this scoping rapid review was to identify and analyse existing qualitative methodologies that have been used to investigate K-12 teachers' lived experiences of adult cyber abuse as a result of student content “going viral” to propose a novel methodological stance incorporating the Australian Online Safety Act 2021. Design/methodology/approach A search of Google Scholar was conducted using keywords and phrases related to cyber trauma, teachers, qualitative methods and the Online Safety Act. Inclusion criteria for the review were: (1) published in English, (2) focused on teachers' experiences of online abuse and cyberbullying associated with viral posts and (3) employed a qualitative inquiry methodology. Full-text articles were obtained for those that met the inclusion criteria. Data were extracted and analysed using a PRISMA flowchart and inductive thematic analysis. Findings This methodology is considered to be justified, as the eSafety Commissioner's Safety-by-Design principles do not have any legal or regulatory enforceability, whereas the Online Safety Act 2021 provides the Australian eSafety Commissioner an avenue to drive greater algorithmic transparency and accountability. Research limitations/implications The findings of this review informed the development of a novel methodological stance for investigating Australian teachers' lived experiences of adult cyber abuse associated with viral posts. It provides a methodological positioning to support trauma informed qualitative research into adult cyber abuse, informed by the work of the eSafety Commissioner and the Online Safety Act. Originality/value Cybertrauma is described as “any trauma that is a result of self- or, other-directed interaction with, mediated through, or from any electronic Internet/cyberspace ready device or machine learning algorithm, that results in impact now or the future” (Knibbs, 2021). It may result from the tracking of movement through various mobile phone features and applications such as location sharing, non-consensual monitoring of social media, and humiliation or punishment through the sharing of intimate images online, through to direct messages of abuse or threats of violence or humiliation. These actions are further perpetuated through automated searches, insights and recommendations on social media (i.e. engagement metrics promote memes, Facebook posts, Tweets, Tiktoks, Youtubes and so on). This is a novel methodology, as it not only considers direct cybertrauma but also automated forms of cybertrauma.
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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 | 0,013 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découle