When We Become Many: Diversity and Collective Responsibility in Christoph Schlingensief’s <i>Chance 2000</i>
Bibliographic record
Abstract
With the launch of his Chance 2000 political party and the accompanying performance during the German federal elections in 1998, Christoph Schlingensief reversed the understanding of responsibility from a help-yourself mentality to a collective attitude, giving marginalized groups political agency. What does it mean to become many; to act, move, and be perceived as a collective? Drawing on Judith Butler’s notions of connectedness, this article proposes a reconfiguration of collective responsibility and argues that performances like Chance 2000 can produce a consequential awareness of individual actions that contribute to the shaping of the collective in the assembly while displaying and maintaining their inherent diversity. Using Karen Barad’s agential realism as a theoretical framework, this article furthermore suggests that Schlingensief’s enactments of collective responsibility produce dynamic connections that have the potential to change the agency of the collective. What happens in the spaces and moments of assembling creates conditions of collective responsibility in the inevitable inclusions and exclusions that these (intra-)actions produce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".