Knowing Persecution When We See It: Non-State Actors and the Measure of State Protection
Bibliographic record
Abstract
Abstract Whether to grant asylum to claimants who are victimized by non-State actors is one of the thorniest questions in refugee law, particularly in Canada. Numerous questions have arisen around how to measure State protection in such circumstances. The result is a convoluted array of legal determinants – a situation that may be placing some claimants at risk. This article attempts to forge a more accessible framework of analysis for non-State actor claims. The suggested framework restores the absence of ‘State protection’ to its traditional role within the refugee definition of the 1951 Refugee Convention – as one prong of a test for persecution, not a stand-alone criterion for refugeehood. Accordingly, it is suggested that decision makers approach non-State actor claims as simply an assessment of whether the situation is one of ‘persecution’, a term that has been defined by leading scholars. By applying these definitions of persecution to typical refugee claim scenarios, it is demonstrated that a persecution-centred heuristic in non-State actor claims provides a clearer and more principled framework of analysis – one that gravitates towards stable and measurable criteria for assessing State protection.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".