Bibliographic record
Abstract
In the last ten years there has been increasing focus on the plight of nonreligious and atheist peoples being persecuted in various countries. Some of this focus has come from cases brought to the attention of atheist/humanist organisations such as Atheist Alliance International (AAI), the International Humanist and Ethical Union (IHEU) and local groups. Cases are being reported by the non-religious themselves, from within countries where their views are not acceptable, potentially ending in imprisonment or death. For example, cases have been reported within Saudi Arabia, Afghanistan, Bangladesh, Pakistan, Morocco and Indonesia. There have been general concerns over the status of non-religious and atheist refugees due to the wording of the 1951 Convention Relating to the Status of Refugees. The UN has recently (2016) confirmed the inclusion of non-religious and atheist refugees under the 'religion' criteria, and some countries, such as the UK, Australia and Canada, have accepted refugees based on persecution due to atheism. However, atheism as a criteria is not clearly accepted by all countries of asylum. This article will look at atheist asylum cases, the need for asylum and the structural difficulties faced by atheist/non-religious asylum seekers.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".