First Nations women's experiences of technology-facilitated abuse in family violence settings: help-seeking and support
Bibliographic record
Abstract
This research explores First Nations women's experiences with technology-facilitated abuse (TFA) within family violence settings; help seeking and supports. This is an all-Indigenous project that situates First Nations women centrally.The scarcity of research currently available examining this continents First Nations women's experiences with family violence and TFA is unmistakable. Within the broader context of family violence, TFA is becoming increasingly problematic for many women. First Nations women already encounter a heightened risk of violence due to colonisation and systemic inequalities and are particularly at risk. This form of abuse weaponises various modes of technology such as social media platforms, mobile phones and other devices to stalk, threaten, monitor and control. The convergence or intersection of gender, Indigeneity, digital literacy and access results in First Nations women experiencing intensification of its occurrence, impact and a reduction in help-seeking pathway options.In a broader sense, this research is intended to be spread across two phases. Phase one (Masters) this thesis, focuses on exploring First Nations women’s experiences and knowledge of TFA in the context of family violence. Phase two (PhD) research project will be heavily informed and shaped by the findings of this Master’s thesis. Throughout, priority is placed on Indigenous research methodologies and methods including yarning (Bessarab & Ng'andu, 2010), two-way learning (Bell et al., 2011) and the cultural practice of weaving. For both phases, I aim to amplify the voices of First Nations women victim-survivors of TFA, with the firm belief that First Nations women know what they need to support them - they just need to be heard (AHRC, 2020).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".