Discourse particles and the syntax of discourse-evidence from Miesbach Bavarian
Bibliographic record
Abstract
This dissertation is concerned with the form, function, and distribution of discourse particles in Miesbach Bavarian. These elements are commonly considered in either semantic, pragmatic, or discourse analytic terms. This current investigation explores the interaction between form, meaning, and distribution of discourse particles, their syntax. I show that discourse particles in Bavarian are constructed, and discourse particles therefore should not be considered as a primitive. ‘Discourse particle’, as I show in this dissertation, is the effect of a unit of language with an invariable core meaning (among them scalar and deictic core meanings) when it associates with a discourse functional syntactic layer that represents the discourse participants’ epistemic states. The claims of this dissertation are empirical at the core; I show conversational data from the Miesbach Bavarian dialect of German that provides the need to distinguish three classes of discourse particles (DPRTs); speaker oriented, addressee oriented, and other oriented DPRTs. I present an analysis that proposes these three classes to be the result of an association with different discourse participants (speaker, addressee, or other). This association serves to ground propositions. In order to model this grounding function of those items interpreted as DPRTs, I make use of the Universal Spine Hypothesis, a framework proposed by Wiltschko (2014). I extend Wiltschko's Universal Spine to include the participant anchor with the projection GroundP.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".