Bibliographic record
Abstract
Abstract In the present article we distinguish the concept of ecology of language as articulated in Mufwene (2001ff) from that of ecolinguistics developed especially by Mühlhäusler (1996ff) , Fill and Mühlhäusler (2001) , Couto (2009) , and several contributors to Fill and Benz (to appear) . We explain how Mufwene’s ecology of language concept, 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 of language 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".