Bibliographic record
Abstract
T ext searching and sorting is one of the most well researched areas in computer science. It is covered in an introductory algorithms course in nearly every engineering school, and there are entire books devoted to the subject. Why then, you might ask, is it necessary to publish yet another article about searching? The answer is that most of the well-known, efficient search algorithms don't work very well in Unicode, which includes the char type in Java. Algorithms such as Knuth–Morris–Pratt and Boyer–Moore utilize tables that tell them what to do when a particular character is seen in the text being searched. That's fine for a traditional character set such as ASCII or ISO Latin-1 where there are only 128 or 256 possible characters. Java, however, uses Unicode as its character set. In Unicode, there are 65,535 distinct characters that cover all modern languages of the world, including ideographic languages such as Chinese. In general, this is good; it makes the task of developing global applications a great deal easier. However, algorithms like Boyer–Moore that rely on an array indexed by character codes are very wasteful of memory and take a long time to initialize in this environment. And it gets worse. Sorting and searching non-English text presents a number of challenges that many English speakers are not even aware of. The primary source of difficulty is accents, which have very different meanings in different languages, and sometimes even within the same language: Many accented letters, such as “é” in “cafe”, are treated as minor variants on the letter that is accented, in this case “e”. […]
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.024 |
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".