In Search of Naming Patterns: A Survey of Finnish Lake Names
Bibliographic record
Abstract
The existence of patterns as one of the factors in the toponomastic process has been known for more than a quarter of a century. However, while some onomasticians have suggested that such patterns can play an important role even when the names in question can be adequately explained by other means, such hypotheses have been rather difficult to prove. The present study is an attempt to address the issue: the goals were, first, to find regularities in the naming of Finnish lakes; second, to assess whether such regularities imply the presence of naming patterns; and third, to see if a quantitative study could give new insights about the properties of such patterns. This was done by applying methods developed in the computer science field of data mining to an electronic corpus consisting of all Finnish lake names found on the 1:20 000 Basic Map. These revealed several groups of names that appear next to each other significantly more often than could be expected, even after accounting for regional variation in the distributions of the names. Some of the groups can be explained by referring to e.g. cultural history, but in a large number of groups the names have a semantic relationship which suggests that there is a large number of relatively widespread patterns in naming Finnish lakes. However, these patterns are very specific and it is difficult to see a systematically productive general pattern. Some of the phenomena involved can be described using Construction Grammar, but it is evident that the theoretical framework needs some adjustments.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".