The Prevalence of Economic Abuse Among Intimate Partners in Alberta
Bibliographic record
Abstract
We examine the prevalence of economic abuse in all demographics. Previous research primarily considers female victims within heterosexual relationships characterized by other forms of intimate partner violence (“IPV”). Consequently, economic abuse may appear to be a less widespread societal issue than it is. Relying on theory that IPV is not a gendered phenomenon, we collected primary data of the prevalence of economic abuse in the Alberta general population and analyzed the influence of demographic variables on the likelihood of experiencing economic abuse. We surveyed 300 random adults in every demographic on what economically abusive behaviors they have experienced and used univariate and regression analysis to determine the effect of different demographic variables on those experiences. We found that 36% of all adults in the sample experienced economic abuse, with 17% experiencing severe economic abuse. Being male or female had no statistical impact on the likelihood of experiencing such abuse, and the effect of income is contrary to previous assumptions. Women are more vulnerable to Economic Control, a subtype of economic abuse. Economic abuse is a broader problem than research to date has considered. Though it is not a gendered phenomenon, different behaviors are more prevalent or severe for different genders. The prevalence of economic abuse in all demographics suggests that further awareness and advocacy is necessary to reduce its incidence. Additional research on a national level is needed to determine patterns and motivations for economic abuse and its correlations with other forms of IPV.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".