Announced but Not Enacted: Anti-Racist German Studies as Process
Bibliographic record
Abstract
Abstract In their February 2019 Forum piece in Applied Linguistics, Bhattacharya et al. (2019) present longitudinal evidence that the American Association for Applied Linguistics (AAAL) still struggles to realize core principles of diversity and advocacy despite the passing of its 2013 resolution ‘Affirming Commitment to Promoting Diversity’ (AAAL 2013). They outline several trends in the available institutional data confirming that inequitable practices continue to hinder Scholars of Color from achieving full recognition within AAAL and, more broadly, the international, interdisciplinary field of applied linguistics. Echoing the work of Bhattacharya et al. in this regard, we reflect on parallel concerns in one of the component disciplinary realms of Applied Linguistics, namely the teaching of German language and culture (sometimes referred to as German Studies).
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.023 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".