Teaching Inclusive Language Against Exclusive Norms: Theoretical, Empirical, and Practical Considerations for the L2 Spanish Classroom and Beyond
Bibliographic record
Abstract
Languages exhibit varying degrees of complexity in their gender systems. Some, like Finnish and English, convey gender distinctions solely through lexical variation (e.g., äiti–isä, mother–father, respectively), while others feature intricate agreement systems encompassing pronouns, adjectives, predicate nominatives, and verbs. Further, some languages already employ gender-neutral pronouns within their traditional linguistic norms (e.g., Finnish uses hän for ‘he,’ ‘she,’ and ‘it’) while others have had to create new pronouns (e.g., hen in Swedish) and new agreement systems (e.g., elle and the corresponding -e gender morpheme in Spanish) that exist beyond gender binaries. Relatedly, languages employing grammatical gender often use the so-called generic masculine to refer to mixed-gender groups of people, presenting issues for women who go linguistically unrepresented in such constructions which have prompted calls and attempts for reform. But where innovation towards inclusion occurs also come issues in using and teaching novel linguistic forms. This article uses the Spanish gender system and its novel inclusive forms, along with some examples from other languages, as a case study for answering two key questions that world language and second language educators must answer as they approach inclusive languages: What do I tell my nonbinary students when they ask what linguistic options they have? and How do I teach language without enforcing gender stereotypes? Recommendations include faithfulness to the morphophonology of the target language, visibilizing linguistically marginalized groups, and, above all, a willingness to engage in discussions about gender. Lastly, sample inclusive pedagogical resources for language teachers are provided.
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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 | 0.021 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".