Iconicity as the motivation for the signification and locality of deictic grammatical tones in Tal
Bibliographic record
Abstract
We present novel evidence for iconicity in core morphophonological grammar by documenting, describing, and analysing two patterns of tonal alternation in Tal (West Chadic, Nigeria). When a non-proximal deixis modifies a noun in Tal, every tone of the modified noun is lowered. When the nominal modifier is a proximal deixis, the final tone of the modified noun is raised. The tone lowering and raising are considered the effects of non-proximal and proximal linkers, which have the tone features [–Upper, –Raised] and [+Raised] as their respective exponents. The realisation and maximal extension of the non-proximal tone features are considered effects of morpheme-specific featural correspondence constraints. Similarly, the exponent of the proximal linker docking on the final TBU is due to the relative ranking of the proximal-specific correspondence constraints. The association of the tone features [–Upper, –Raised] and [+Raised] with non-proximal and proximal linkers, respectively, is in line with crosslinguistic patterns of magnitude iconicity. Given that the local and long-distance realisations of the proximal and non-proximal featural affixes respectively are perceptually similar to deictic gestures, the locality of the featural affixation is considered a novel pattern of iconicity. To motivate this pattern of iconicity, we extend the notion of perceptual motivation in linguistic theory to include the crossmodal depiction of sensory imagery. Consequently, Tal presents evidence for iconicity as a motivation for morphophonological grammar.
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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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".