Bibliographic record
Abstract
Subject Code Description Nr of Studies on this Subject N9 REGION IN NATION 1 N9.1 Area in nation (geographic region) 3 N9.1.1 Earlier area of residence 1 N9.2.2 Current area of residence 3 N9.2.2.1 . region in Australia 4 N9.2.2.2 . region in Canada 3 N9.2.2.4 . region in Denmark 4 N9.2.2.5 . region in Germany 9 N9.2.2.6 . Region in Italy 1 N9.2.2.7 . region in Israel 1 N9.2.2.8 . region in Netherlands 2 N9.2.2.9 . region in Nigeria 2 N9.2.2.10 . region in Norway 3 N9.2.2.11 . region in UK 1 N9.2.2.12 . region in USA 8 N9.3 Attitude climate in region 0 N9.3.1 Happiness in region 0 N9.3.2 Hope in region 0 N9.3.3 Satisfaction in region 0 N9.4 Characteristics of region 0 N9.4.1 Demographic composition of region 0 N9.4.1.1 Age composition in region 1 N9.4.1.2 Divorced in region 1 N9.4.1.3 Widowed in region 1 N9.4.2 Ecological sitruation in region 0 N9.4.2.1 Nature in region 1 N9.4.3 Economic situation in region 0 N9.4.3.1 . economic affluence in region 1 N9.4.3.2 . unemployment in region 4 N9.4.5 Pollitical conditions in region 0 N9.4.5.1 .political violence 0 N9.4.6 Education in region 0
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 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".