The Preposition (fii) in the Horizontal and Vertical Axes as Used in the Taizzi Dialect: A Cognitive Approach
Bibliographic record
Abstract
In this paper the framework of Cognitive Grammar (CG) developed by Langacker is adopted to attain a cognitive semantic analysis of the use of the Arabic prepositions (fii) in the horizontal and vertical axes, as used in the Taizzi dialect. Although, encoding the sense of CONTAINMENT, the preposition (fii) is assumed not to play any role in the horizontal and vertical axes; the use of the preposition (fii) in the TD proves things differently. The problem with (fii) is that it is very tempting to be used in the locative sense in which one physical entity is CONTAINED WITHIN another physical entity. However, the cognitive analysis of (fii) justifies the use of this preposition in many instances of the Taizzi dialect where this preposition is seemingly exploited to encode non-containment-related spatial relations. This unfolds some of the unsolved issues concerning prepositions in general and the Arabic prepositions in particular taking the use of (fii) in the Taizzi dialect as a sample. The data presented in this paper show that speakers of the Taizzi dialect extend the use of (fii) to depict spatial relations other than the ones where the Trajector (TR) is actually contained within the boundaries of the Landmark (LM). The instances analyzed in this paper show that (fii) encodes spatial relations in which the TR and the LM are horizontally or vertically related to each other. However, the use of the preposition (fii) by speakers of the Taizzi dialect to encode these spatial relations proves they cognitively characterized the LMs as containers that contain the TRs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".