Bibliographic record
Abstract
A distinguishing feature of the cognitive process of speech planning is its flexible balancing of speed, quality, and effort. Utterance planning strategies can vary adaptively depending on speaker goals and circumstances. For example, when speed is a priority, the planning process might sacrifice the quality of an utterance by engaging in more incremental, on-the-fly planning. A focus on utterance quality may require more time. But sometimes, speakers seem to plan utterances well in advance without sacrificing quality or speed. In this chapter, we focus on recent research that explores how working memory can foster the flexibility of speech planning strategies. We review the role that WM might play in individual levels of planning, including message planning, grammatical encoding (including lemma selection and structure building), and phonological encoding, and the extent to which the scope and quality of planning at these different levels could be subject to WM constraints or predicted by WM capacity. We conclude that WM is a (sometimes optionally invoked) part of a complex system of compensatory factors that can determine how speech planning unfolds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".