Bibliographic record
Abstract
The main goal of this paper is to compare search for and look for in the TV Corpus (TVC), the Movie Corpus (MC), the Corpus of Contemporary American English (COCA), and the British National Corpus (BNC). When it comes to the TV Corpus, it is interesting to point out that look for was preferable to search for in the TV programs of America, the UK, Canada, Australia, New Zealand, and Ireland. A further point to note is that the frequency of search for (1,898 tokens) and look for (5,423 tokens) reached a peak in the 2010s. With respect to the Movie Corpus, it is interesting to note that look for was favored over search for in the movies of six countries. More interestingly, search for (515 tokens) and look for (2,259 tokens) reached a peak in the 2010s. The COCA clearly shows that search for truth (369 tokens) and look for ways (566 tokens) are the most preferred by Americans. It is significant to note, on the other hand, that 36.36% of forty four nouns are the collocations of both search for and look for in the COCA. Similarly, the BNC shows that search for evidence (19 tokens) is the most commonly used one in the UK, whereas look for work (34 tokens) is the most widely used one. Finally, it is noteworthy that 17.64% of fifty one nouns are the collocations of both search for and look for in the BNC.
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".