Creating a Gender-inclusive Learning Environment in Spanish Language Classrooms
Bibliographic record
Abstract
Non-binary speakers often struggle to describe themselves within the binary gender system of Spanish. This is even more of a challenge for learners of Spanish who are used to describing themselves in English without having to identify their gender. Our study investigates how we can create a more inclusive learning experience in our Spanish language classrooms. To answer our research question, we conducted a needs analysis through the distribution of an online survey and a series of 20-minute interviews with students and instructors of Spanish in Western Canada. Through these methods, we determined what participants already know about gender-neutral and inclusive language, and what they want or need to know to be able to use and teach inclusive language. Participants were also given the opportunity to share their suggestions and ideas as to how to create a more inclusive learning environment. The key findings indicated that both instructors and students need education and training, and access to teaching and learning resources to normalize the use of gender-inclusive language in the Spanish classroom, which in return will help foster a learning space that is inclusive of all students.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".