Animating the urban: between infrastructure and encounter
Bibliographic record
Abstract
Cities play an increasingly crucial role in addressing the accelerating planetary biodiversity crisis. In this special issue, the authors offer generative tools grounded in an other-than-human standpoint inviting us to “think cities” differently. They re-examine the right to the city and a more-than-human commons; evaluate why and when species become “killable”; and rethink territoriality, attending to the ways other-than humans make and remake cities. They reconceptualize the urban as an ecological formation, entangling cultivated, feral and wild systems of governance. They explore policies that fix our views of other-than-humans, and enlist them in human conflicts, disavowing the fluidity of animal lives. They expose the ways other-than-humans suffer disastrous consequences of urban greening policies when not taken into account. Together they demonstrate why urban theorists, must take as starting point Levi-Strauss’ ([1971]. Totenism. Beacon Press, p. 89) admonition that “animals are good to think [with].”
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".