Bibliographic record
Abstract
ICTs such as mapping platforms, algorithms, and databases are a central component of how society responds to the threats posed by disasters. However, these systems have come under increasing criticism in recent years for prioritizing technical disciplines over insights from the humanities and social science and failing to adequately incorporate the perspectives of at-risk or affected communities. This paper describes a unique month-long workshop that convened interdisciplinary experts to collaborate on projects related to flood data. In addition to findings about the practical accomplishment of interdisciplinary collaboration, we offer three interrelated contributions. First, we position interdisciplinarity as a critical practice and offer a detailed example of how we staged this process. We then discuss the benefits to interdisciplinarity of expanding the range of temporal logics normally deployed in design workshops. Finally, we reflect on approaches to evaluating the event's contributions toward sustained critique and reform of expert practice.
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 | 0.035 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.004 | 0.035 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".