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
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".