<scp>Sol Steinmetz and Barbara Ann Kipfer</scp>, <i>The life of language: The fascinating ways words are born, live, and die</i>
Bibliographic record
Abstract
Sol Steinmetz and Barbara Ann Kipfer, The life of language: The fascinating ways words are born, live, and die. New York: Random House, 2006. Pp. xii, 388. Pb $16.95 In 1862, Max Müller published the first of his two volumes on The Science of Language, lectures delivered at the Royal Institution the previous year. The books are among the very earliest examples of “popular” discussions of important linguistic issues, and no doubt the first by a distinguished scholar. They set a standard that has been very frequently emulated, but very rarely equaled. We see in our own time an avalanche of popular books on language, all aimed at a broad and continuing interest that is hardly surprising in a subject of such immediate and ubiquitous presence. Of course, the quality varies immensely. There are basic problems even with the best of them, and the most basic of all are the reconciliation of an appealing scope with a reasonable amount of depth and, relatedly, the provision of some thematic continuity in treatments typically composed of a large number of entries. The most common results, then, are books that are something like dictionaries, something like encyclopedias and, often, something like cabinets of curiosities.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.150 | 0.105 |
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".