Bibliographic record
Abstract
Abstract A topic-wa-phrase is analyzed here in written recipes and the corresponding spoken instructional cooking discourse. Despite the possible assumption that the topic phrase is not used in recipes, the analysis shows that ingredients of a recipe are selectively topicalized. Those topicalized are the primary ingredients which are given with substantial procedural descriptions, when these procedures represent a parallel relationship to each other. The topicalization connects the parallel segments so that they constitute coherent discourse and properly represent the intended structure of the recipe. Also, in the spoken discourse, a topic-wa-phrase is used in a side-sequence (a digression) to connect with the main segment. These functions of representing important information and connecting parallel elements are consistent with observations in other genres of discourse. On the other hand, the analysis also suggests discourse type-based variations. In the instructional discourse, the “digressions” are connected as part of the coherent discourse. This contrasts with (non-instructional) casual spoken discourse, in which digressions are not topicalized and are detached from the main segment. These variations imply speakers’ different pragmatic intentions based on different types of discourse, which are reflected on their choice of referential forms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".