Between and Beyond <i>Er|Sie</i> : Trans and Non‐Binary Identities in the L2 German Classroom
Bibliographic record
Abstract
German culture is currently in conflict over Gendern, which Gallagher (2025) defines as "the creative use and modification of the language to be more inclusive of women, queer, and nonbinary people," and the German language itself has been placed front and center of the debate.What does this mean for us as teachers of German language and culture, for our classrooms, and our language use?Maybe it is best to start with our understanding of what language is and is not.When encouraging students to memorize vocabulary and conjugate verbs, language may start to feel stable and concrete.However, language is not a static, singularly codified symbolic system performed uniformly by "native" speakers as is sometimes reflected in teaching and reference materials.These materials, as well as the ideologies about language they espouse, defer to a dominant variety of language that has a certain level of prestige, that is, the so-called "Standard" language; in our case, "Hochdeutsch."However, any German textbook, dictionary, or grammar captures just a still frame of a language in time and just a subset of mostly preferred usages from the vibrant diversity actually in use.The codification and prestige allotted to this particular still frame inevitably lead to the view that there is a "correct" version of German, which is highly influenced by dominant and majority cultural beliefs.In reality, language is a complex, dynamic system that emerges out of communicative needs, historical processes, and cognitive processing played out in the interactions of millions of speakersfirst-language and second-language speakers alike.As humans, we have a relatively narrow timespan in relation to the languages we use, so it is easy to mistakenly understand language as something unchanging.Gendern, then, is but one example of language changing to meet new communicative needs.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".