Language and Nature in Southern and Eastern Arabia
Bibliographic record
Abstract
This paper examines the relationship between language and nature in southern and eastern Arabia. The work is the result of a two-year interdisciplinary network between the University of Leeds and Qatar University, with partners in the UK, Oman, Canada, the United States, and Russia. Our hypothesis is that local languages and ecosystems enjoy a symbiotic relationship, and that the demise of local ecosystems will adversely affect local languages. In this paper, we examine some of the language-nature effects in Qatar and Dhofar, southern Oman. Our regions differ in that Qatar has two seasons, summer and winter, and is predominantly arid, with occasional rain, while Dhofar together with al-Mahrah in eastern Yemen has four distinct seasons, receiving the monsoon rains between June and September, and, as a result, is home to hundreds of plants and animals found nowhere else in the world. Since the 1970s, in particular, both regions have experienced some of the most rapid socio-economic changes in the world. We ask what affect this socio-economic change has had on the language-nature relationship, and suggest that decoupling of the human-nature relationship as a result of socio-economic change is contributing in these regions to language attrition. We consider spatial terminology, traditional terminology for weather, the traditional measurement of time by narratives around key climatic events, and the role of stars in determining the weather and their role in folklore.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".