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 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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".