Bibliographic record
Abstract
SQL is a standard language used for defining, manipulating, and querying relational databases. This appendix provides a brief discussion of how basic queries conveyed in SQL may be expressed in LogiQL. Consider the following relational database, which includes two relational tables (Tables H.1 and H.2) describing countries in 2011. The country table lists the ISO two-letter code, name, and population of various countries. For discussion purposes, the population of Finland (5,396,292) is omitted simply to illustrate SQL’s use of a null value to indicate that a data value is missing (e.g., because it is unknown or inapplicable). To save space, only a small number of countries are included. For those countries that have presidents, the president table lists the name, country, gender, and birth year of those presidents. Australia, Canada, and the United Kingdom have prime ministers instead of presidents, so they are not included in the president table. Throughout the database, countries are standardly identified by their country codes. The entries in the first two columns of the tables are necessarily unique, so each of these columns is a candidate key for its table. The country table has countryCode as its primary key and countryName as an alternate key.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.096 | 0.077 |
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".