<i>Auf den Spuren ihrer Geschichte</i>: Black German Detectives and the Cases of Anäis Schmitz and Fatou Fall
Bibliographic record
Abstract
The various types of detective work that Black individuals and communities undertake enables them to collect evidence and knowledge and disseminate tactics for resistance. I first contextualize the advent of Black German detection in real life and then turn to fictional Black German detectives in literature and on television. In doing so, I explore the interconnectedness of Black German belonging—both real and imagined—through the act and art of detection. The earliest official, fictional Black German female detectives within these media, Anäis Schmitz and Fatou Fall, allow for an exploration of the intersectionality of race, cisgender, and heteronormative reproduction. Their introduction to this genre overlaps in the timing of their debut appearances (2019), but the characters contrast in terms of representation, due in part to the medium employed and to the racial positionality of their creators. Thus, I investigate how crime novels and crime television shows shift the representation and recognition of Black Germans but remain attuned to how institutional and historically anchored racist structures “frame” Black German belonging.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".