Bibliographic record
Abstract
was naming the beasts and birds. The book of Genesis doesn’t tell us whether he found this task difficult or burdensome, but today the need to name and number things has become a major nuisance. When you try to choose a name for a new Internet domain or an e-mail account, you’re likely to discover that your first choice was taken long ago. One Internet service tells me the name “brian ” is unavailable and suggests “brian13311 ” as an alternative. Perhaps I should think of this appellation in the same category as Louis the 18th or John the 23rd, but being Brian the 13,311th seems a dubious distinction. The challenge of inventing original names is particularly acute when the name has to fit into a format that allows only a finite number of possibilities. For example, the ticker symbols that identify securities on the New York Stock Exchange can be no more than three characters long, and only the 26 letters of the English alphabet are allowed. The scheme imposes an upper limit of 18,278 symbols. If the day ever comes that 18,279 companies want to be listed on the exchange, the format will have to be expanded. And long before that absolute limit is reached, companies could have a hard time finding a symbol that bears any resemblance to the company name. It’s not just names that are scarce; we’re even running out of numbers. A few years ago telephone numbers were in short supply, and so were the numbers that identify computers on the Internet. Those crises have abated, but now attention has turned to the Universal Product Code, the basis of the barcode labels found on virtually everything sold in the United States and Canada. It seems the universe has more products than the UPC has code numbers. For that reason and others, the 12-digit UPC standard is being supplanted by a 13-digit code, with provisions for adding a 14th digit. The “sunrise” date for this transition is January 1, 2005. The old 12-digit codes will continue to be recognized, so you may not notice an immediate change on product labels, but every supermarket and drug
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.037 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.037 | 0.037 |
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".