Development of tense/aspect in Semitic in the context of Afro-Asiatic languages
Bibliographic record
Abstract
The author applies the comparative method for the reconstruction of earlier aspectual systems in the Afro-Asiatic phylum of languages. Moving ‘upstream’ from the documented systems of Semitic, Berber and Old Cushitic the state of affairs during the common stage of Proto-Semito-Berbero-Cushitic is reconstructed. With the addition of Egyptian and Chadic data important conclusions regarding the elusive Proto-Afro-Asiatic are reached. Moving ‘downstream’ the trajectory of individual aspectual systems through their later stages is analyzed. A central piece of the monograph is the reconstruction of intermediate stages reflecting the long-term developments of aspectual and temporal categories of individual languages from the Old towards their Middle periods. The continuity and innovation in the aspectual systems towards the contemporary state of affairs in analytic (serial) constructions of Modern Aramaic and Arabic vernacular languages is explicated. The author demonstrates that it is imperative to work in a larger typological framework and that in the field of Afro-Asiatic linguistics valuable insights can be gained from the study of parallel phenomena in Indo-European languages. At the same time, Indo-Europeanists will profit from the study of typologically earlier aspect-prominent systems of Afro-Asiatic languages. The monograph offers important contributions to our understanding of universals and to the typology and diachrony of tense and aspect.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".