Bibliographic record
Abstract
The Research Unit Emerging Grammars (RUEG) investigates the linguistic systems and linguistic resources of bilingual speakers from families with an immigrant history, “heritage speakers”, in both of their languages across different language pairs, registers, and age groups. We investigate speakers of Russian, Turkish, and Greek as heritage languages in Germany and the U.S., in addition to German as a heritage language in the U.S., as well as monolingual controls for majority and heritage languages. We study noncanonical phenomena as indicators of new grammatical options in bilingual systems. All projects contribute tothree “Joint Ventures” targeting (1) the development of new dialects vs. incomplete acquisition or erosion (“Language Change Hypothesis”), (2) the relevance of internal vs. external grammatical interfaces (“Interface Hypothesis”), and (3) the distinction of contact-induced change vs. language-internal developments and variation (“Internal Dynamics Hypothesis”). As a result of our collaborative research, we expect new insights into the special dynamics of language variation, language change and linguistic repertoires in contact situations and the modelling of noncanonical structures in the grammatical system, and new impulses for the investigation of heritage speakers and of speakers’ resources. The projects are supported by two Mercator Fellows: Shana Poplack, University of Ottawa Maria Polinsky, University of Maryland Jeanine Treffers-Daller, University of Reading Cristina Flores, Universidade do Minho The Research Unit is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project numbers: 39482131 & 313607803. You can search this data online in ANNIS: https://korpling.german.hu-berlin.de/annis/
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.285 | 0.173 |
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".