Devising the Policy Tools of a Human Rights-Based International Protection System
Bibliographic record
Abstract
The Global Compact on Refugees (GCR) has brought new hopes and new controversies around international refugee protection. Although it does not replace the 1951 Geneva Convention on the Status of Refugees (Convention) but adds to it a framework to facilitate voluntary responsibilitysharing among states, there are divergent views about its potential impact: For some, it has a realistic potential to improve refugee protection (Türk and Garlick 2016), and it potentially increases states’ protection commitments (Betts 2018). For others, it is too thin to address the current human rights and refugee protection challenges (Hathaway 2018), and it dilutes the right to seek asylum stipulated in the Convention (Chimni 2018). In PROTECT, a Horizon 2020-project with 12 partner universities located in Europe, Canada, and South Africa (https://protect-project.eu), we posit that GCR can be a new window of opportunity to improve the international refugee protection system if proper means are deployed in its implementation.
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 | 0.050 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".