Report on the first International Summer Institute for Interactional Linguistics at the IDS Mannheim, 18th – 23rd of July 2022
Bibliographic record
Abstract
The first International Summer Institute for Interactional Linguistics (henceforth ISIIL) took place from July 18 to 23 at the Leibniz-Institute for the German Language (IDS) in Mannheim, Germany. The local organizers, Arnulf Deppermann and Alexandra Gubina, collaborated with five other facilitators in preparing this Summer Institute: Emma Betz (University of Waterloo), Elwys De Stefani (University of Heidelberg & KU Leuven), Barbara A. Fox (University of Colorado), Chase Raymond (University of Colorado) and Jörg Zinken (Leibniz-Institute for the German Language, Mannheim). The goal of ISIIL was to bring together both early-career researchers and established scholars from the fields of Conversation Analysis (CA) and Interactional Linguistics (IL) in order to foster the development of new skills for doing research using IL. The participants and organizers had diverse backgrounds, both in terms of their research interests (e.g., classroom interaction, second language acquisition, cross-linguistic comparison, particles, grammar-in-interaction) and institutional affiliations, with many participants from institutions from around Europe (i.e., Belgium, Denmark, England, France, Germany, Norway, Sweden, Switzerland) as well as overseas (Canada, U.S.A., South Africa). Because of the compact nature of the Institute, the advanced topics covered, as well as the original research projects the participants would engage in, participation was limited to 24 participants, selected on the basis of their prior training and experience in CA/IL.
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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 | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.225 | 0.134 |
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".