“Unsure whether it feels like home anymore”: Unsettled feelings amongst former panel-block residents in Moscow and Berlin
Bibliographic record
Abstract
In 2017, Moscow launched an urban renewal campaign proposing to tear down eight thousand panel-block apartment buildings aiming to relocate over a million and a half residents into newly built high-rises. The campaign provoked diverse reactions ranging from anger to excitement for the chance to improve living conditions. The author began ethnographic research on the eve of the first phase of demolition in 2021, inviting Moscow-, and Berlin-based artists to examine the stories, memories, and feelings of their panel-block homes in a collaborative visual anthropology research project. In 2022, Russia’s full-scale invasion of Ukraine interrupted research, beckoning the author to reevaluate their study from affective and ethical positions. Studying responses to the war using autoethnographic methods allowed the author to acknowledge disaffection and withdrawal as significant emotional responses to the threat of political violence—dispositions that make for ethnographically and ethically important moments of research.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →1 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.
Ethnography that turns substantially into reflexive analysis of ethnographic practice and research ethics under political violence; borderline, since the substantive object remains housing and home.
The ethnography studies residents' feelings and responses to urban renewal and war, not research itself.
Urban ethnography of housing and belonging, not science or research as objects.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".