Referential communication strategies as a function of accessing conceptual representations of abstract shapes in a second or foreign language
Bibliographic record
Abstract
This thesis investigates referential communication strategies using the theoretical framework of Levelt's (1989) model of speech production and its application to second language speech production (de Bot, 1992). The investigation focuses on utterances of adult speakers who solve a referential communication task in their first language (English) and their second language (German). Two different groups participate in the study. The first group consists of students from a large West Canadian University who learn German in a foreign language classroom, whereas the second group consists of native speakers of English who work in Germany and acquire German in a second language environment. All participants describe abstract shapes while their utterances are being recorded and later transcribed for analysis. Quantitative and qualitative methods measuring the time and words reflect the strategies used to describe the shapes. The analysis of the data reveals that the participants in Germany are more accurate in their descriptions and solve the referential problems more successfully than the participants from the West Canadian University do. The direct contact these participants have with the German language and culture proves to be beneficial to the process of generating a comprehensible message in referential communication. The study concludes with suggestions for the teaching of German as a second language and recommendations are made for future research on language acquisition the learning environment.
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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 | 0.002 | 0.023 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".