Bibliographic record
Abstract
We pre-register two experiments included in the same survey of 5 countries (US, Canada, UK, France, and Germany). The experiments are separated by several batteries. They both aim to examine cross-national differences in how people in the US and Europe (Canada is somewhat ambiguous) react to conflicting information about immigrants, ie that they either satisfy cultural but not functional (employment, self-sufficiency) criteria for assimilation or vice versa, that they satisfy self-sufficiency but do not meet many citizens' criteria for cultural fit. One experiment deals with a hypothetical immigrant individual from Africa. The other deals with a policy that would hypothetically admit 50,000 immigrants from the Middle East.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.093 |
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; both teacher heads agree on what is shown here.
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".