Mobility Struggles in Berlin 2.0
Bibliographic record
Abstract
In July 2024, four years after my last visit that took place shortly before the outbreak of the COVID- 19 pandemic, I spent three weeks in Berlin. During those years, especially in the early stages of the pandemic, I kept in touch with some activists and organizations. I aimed to understand the new challenges that migrants faced in camps where they were more exposed to health risks and often had to comply with strict police- enforced quarantine rules. A few weeks before the 2024 trip, I messaged several activists to arrange a meeting. I realized then that most of the West African activists I had spent time with in 2018 – Bastian, Charles, Manuel, and Robert, for example – had changed their phone numbers and did not respond to my emails. Paul, whom I contacted on Facebook, told me he had also lost touch with all the other activists. After I explained that I intended to catch up and update my research in view of the publication of this book, he did not express much interest in meeting. I emailed Corasol's general address to enquire about attending one of their weekly meetings in Berlin. Someone replied anonymously, explaining that the group was not very active and had stopped meeting regularly since the COVID- 19 pandemic. The week before my arrival, William Chedjou, a man from Cameroon, was killed in the Berlin neighbourhood of Gesundbrunnen by a stranger following a dispute over parking. I saw on a Telegram channel that several organizations had called for a demonstration on 20 July to demand justice for William. They framed the murder as motivated by anti- Black racism and condemned the lack of effective investigation into the suspected racial motive.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".