Bibliographic record
Abstract
Abstract In the present article we distinguish the concept ofecology of languageas articulated in Mufwene (2001ff) from that ofecolinguisticsdeveloped especially by Mühlhäusler (1996ff) , Fill and Mühlhäusler (2001) , Couto (2009) , and several contributors to Fill and Benz (to appear) . We explain how Mufwene’secology of languageconcept, inspired primarily by macroecology, applies to language evolution. We articulate various factors internal and external to a language that bear on how it emerged phylogenetically, underwent particular structural changes, and, in some cases, may have speciated into separate varieties. The external ecology also influences the vitality of languages, rolling the dice on whether they thrive or are endangered. Because these particular phenomena have been elaborately discussed in Mufwene’s earlier publications, we devote more space to explaining how the notion oflanguage ecology, as others call it, also applies as a useful heuristic tool to qualitative sociolinguistics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".