Bibliographic record
Abstract
Earlier this month, the UNHCR announced Syria’s refugee population had reached a staggering three million, now the largest refugee population in the world. The bulk of refugees are resettled or in camps in neighbouring countries, mainly Lebanon and Jordan. Syrians taking the incredibly treacherous journey to Europe by foot or by boat, face complex and varying immigration policies. As the refugee crisis worsens, many fear that Europe’s slow and uneven reaction will exacerbate the strain on states in the Middle East. As part of our ‘Expert Voices’ series, Multimedia Editor, Cheryl Brumley, asked four high-profile experts for their thoughts on what Europe should be doing to help ease the crisis: EU Commissioner for International Cooperation and Humanitarian Crises, Kristalina Georgeiva, the European University Institute’s Philippe Fargues, UNHCR representative, Andrej Mahecic, and Iliana Savova from the Bulgarian Helsinki Committee.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".