Governing sinking worlds: sensemakings of subsidence in Rotterdam, The Netherlands
Bibliographic record
Abstract
The neighborhood of Bloemhof in Rotterdam-South is often presented to be sinking because of soil subsidence. The City of Rotterdam makes use of participatory methods to involve a wide range of stakeholders in Bloemhof and to build consensus on how to deal with the subsiding neighborhood. However, what remains unknown is how civil society, civil servants, and subsidence policy programs actually make sense of subsidence. Therefore, we address the question: How do residents, civil servants, and policy programs make sense of subsidence in Bloemhof, Rotterdam-South? We followed an ethnographic approach, focusing on conversational interviews, events, and policy documents, to uncover the often taken-for-granted ways, unexamined assumptions, and consequences of how subsidence is performed differently across actors and domains. We present four main themes characterizing subsidence sensemaking of residents, civil servants, and policy programs in Bloemhof, showing how (1) most only notice discursive cues of subsidence, while relying on remote sensing tools to make subsidence materially visible (cues of subsidence); (2) how the municipal subsidence efforts in Bloemhof are publicly communicated as open-ended, while internally enacted as resistant to political debate (uncertainty and open-endedness); (3) how subsidence is made sense of as temporally distant, yet enacted as requiring immediate responses (subsidence temporalities); and (4) how municipal subsidence efforts are tinkered with to address other matters of concern (institutional tinkering). With this analysis we contribute to sensemaking theory, and hope to attune practitioners’ sensibilities to reflexivity, by showing how particular science-based sensemaking enacts specific realities of subsidence that constrain the enactive capacity of other meanings (i.e., of residents). Broadening the policy space for multiple meanings may help us better connect with diverse (i.e., social, economic, public) domains, human/non-human actors, and material concerns when governing environmental change, in Bloemhof and beyond.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".