Tough on Terror, Short on Nuance: Identifying the Use of Force as a Basis for Excluding Resisters Seeking Refugee Status
Bibliographic record
Abstract
The use of force has been a significant feature of many political struggles and resistance movements. The consequences for its participants may include the possibility of persecution, if not death. Some will flee and seek protection under the auspices of the 1951 Convention Relating to the Status of Refugees. Since the attacks of September 11th 2001, governments in Australia, Canada and the United States have passed broad national security legislation that effectively renders such persons inadmissible or excluded for the purposes of acquiring refugee status. Regardless of context, the targeting of government actors and the use of proportionate means, all political violence under such legislation becomes invalid. In this article, the author takes the position that such legislation should be repealed. In its place, Article 1F(b) of the Convention can be used to exclude those who engage in serious non-political crimes while allowing those who perpetrate legitimate political crimes to obtain refugee status. Article 1F(b) is the perfect tool as the purpose of the provision was to protect political resisters while excluding those who failed to observe the distinction between civilians and legitimate targets or who adopted disproportionate means and methods. Prevailing political crimes jurisprudence demonstrates that courts and tribunals possess the capability to differentiate between uses of force that are legitimate while rejecting those that are not. They have done so by engaging in nuanced and contextual analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".