Sociocultural theory and the teaching of second languages
Bibliographic record
Abstract
Introduction - Fundamental Concepts and Principles of Sociocultural Theory James P. Lantolf, Pennsylvania State University, & Matthew E. Poehner, Juniata College 1. Both Sides of the Conversation: The Interplay between Mediation and Learner Reciprocity in Dynamic Assessment, Matthew E. Poehner, Juniata College 2. The Effects of Dynamic Assessment on L2 Listening Comprehension, Ruima Ableeva, Pennsylvania State University 3. Changing Examination Structures within a College of Education: the Application of Dynamic Assessment in a Pre-service ESOL Endorsement Program, Tony Erben, Ruth Ban, Robert Summers, University of South Florida 4. Project-based Learning, Autonomy, and Identity, Leo van Lier, Monterey Institute for International Studies 5. Embodied Language Performance: The ZPD and Drama in the Second Language Classroom, Steve McCafferty, University of Nevada, Las Vegas and John Haught, Wright State University 6. A Dialogic Approach to Teaching L2 Writing, Holbrook Mahn, University of New Mexico 7. Revolutionary Pedagogy: Learning that Leads Development in the L2 Classroom, Eduardo Negueruela, University of Miami 8. Materializing Linguistic Concepts through 3-D Clay Modeling: A Tool-and-Result Approach to Mediating L2 Spanish Development, Maria Serrano-Lopez, The Palm Key Institute and Matthew E. Poehner, Juniata College 9. From the Abstract to the Concrete: Vygotsky meets Halliday in the L2 Writing Class, Marilia Ferreira and James P. Lantolf, Pennsylvania State University 10. Mediation as Objectification in the Development of Professional Discourse: The Case of International Teaching Assistants, Steven L. Thorne, Jonathan Reinhardt, & Paula Golombek, Pennsylvania State University 11. Languaging: An Essential Element of Second Language Learning, Merrill Swain and Sharon Lapkin, Ontario Institute for Studies in Education of the University of Toronto 12. Service-learning and Sociocultural Theory: The Impact of Active Contribution on Learner Development, Howard Grabois, Purdue University 13. The Unfulfilled Promise of Language Teaching for Communicative Competence, Sally Sieloff Magnan, University of Wisconsin, Madison 14. Redesigning a B.Ed. Practicum According to Vygotskyan Principles: Pre-service Immersion Student-teachers in Australia, Tony Erben, University of South Florida
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".