Bibliographic record
Abstract
Traces the non-linear dynamic longitudinal L2 development of word search sequences in informal social interaction using CA-for-SLA and Complexity Theory Provides an analytical framework which can be applied to spoken communication in any setting Explains and unites two high-profile research methodologies: Conversation Analysis and Complexity Theory Draws on 37 hours of video-recorded social conversations across two years between students from a range of countries including: Botswana, Canada, China, Japan, Korea, Indonesia, Romania, Thailand, the United States, Uzbekistan, and Vietnam Demonstrates longitudinal development of L2 interactional competence of Japanese learners of English through informal English as a Lingua Franca (ELF) interaction Watch the introductory film about the book Human Spoken Interaction as a Complex Adaptive System explains how human spoken communication functions, combining two separate complex adaptive systems: the universal ‘interaction engine’ and language(s), which now number around 7,000. Siegel and Seedhouse offer a comprehensive overview of how the components and processes of the interaction engine work together to enable us to understand each other, whatever the language. Through combining Complexity Science and Conversation Analysis, this book explains how to simultaneously analyse spoken interaction on micro and macro scales. Detailed analyses of L2 learners reveal them to be simultaneously expert in using the interaction engine and inexpert in using the specific language. The study shows that the basic characteristics of the interaction engine are the same as for other life-related complex systems and that it is possible to access the perspectives of participants inside this complex adaptive system as it is evolving.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".