Bibliographic record
Abstract
De Laet [1643] on Hugo Grotius: If you are willing to change letters, to transpose syllables, to add and subtract, you will nowhere find anything that cannot be forced into this or that similarity; but to consider this as evidence for the origin of peoples – this is truly not proved as far as I am concerned. (Cited in Metcalf 1974:241) Beyond the comparative method? As we have seen in previous chapters, the criteria for establishing genetic relationships among languages were generally clear, and widely known and applied, with reliance on basic vocabulary, sound correspondences, and patterned grammatical evidence of particular sorts – where the comparative method played a central role. Nevertheless, a number of scholars have recently expressed dissatisfaction with what they perceive to be limitations of the traditional methods. “Since the tried-and-true Neogrammarian comparative method can only reach back a few thousand years before the evidence fades out, something else must be tried,” so declares Johanna Nichols (1996b:267), and recently she and others, recognizing the limitations of the comparative method, have proposed differing ways to see past them. While this goal is an appropriate one, none of the alternative approaches proposed to date has achieved success. In this chapter we assess several of these to show why they do not really reach beyond the limitations of the comparative method.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.033 |
| Scholarly communication | 0.009 | 0.031 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".