Bibliographic record
Abstract
Oh okay well okay then I guess that is my destination. (Trevor Klinke, 20) When a person starts a sentence, which words come out first? According to the grammar books of English, you start a sentence with a subject, e.g. I , she , Sali , my daughter , in order to get sentences such as, e.g. I like cats , Sali likes cats , My daughter likes cats. But that is not what happens in spoken language. Sentences do not always unfold with a subject first. What typically happens is more like: You know, I like cats , So, I like cats , or even So, you know, I think, I like cats. Everyone thinks that it is teenagers who do this, and if not teenagers, people who are uneducated or inarticulate. In this chapter, I explore the words that come before sentences, including words such as so , well , you know , uh , as in (79). I will refer to these words as “sentence starters,” SS. a. So uh. Well I really loved it. You know uh I enjoyed it. (William Carlsburg, 82) b. Oh okay yeah so because you grow up with it, you just don't even hear it. (Ken Smuckers, 53) In the literature, these words have been included under the general umbrella of “discourse markers” (DMs) (e.g. Jucker and Ziv, 1998; Schiffrin, 1987), “pragmatic markers” (e.g. Andersen, 2001; Brinton, 1996; Erman, 2001), or “discourse-pragmatic markers.” A question that arises is: Are these all the same type of markers? Why are such words sometimes referred to as “discourse markers,” at other times “pragmatic markers,” and sometimes as “particles”? Aside from being words and phrases that have been disparaged as meaningless and bad, what else can be said? Traditionally, the words in (79) were thought to be empty, meaningless fillers. In the mid 1970s Longacre (1976) referred to them as “mystery particles,” in part because at the time no one really knew what function they served in the language. As we shall see, these words are actually a multifarious set of phenomena and an important part of the grammar of spoken language. There is a great deal to be learned yet, then, about the interrelations that exist between syntax and semantics, and about the way in which the syntactic structure of informal spoken language can best be analyzed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.020 |
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".