Warum denn gleich ontologisieren? Und wenn nicht, warum dann ANT? Kommentar zu Alexa Färbers „Potenziale freisetzen“
Bibliographic record
Abstract
Akteur-Netzwerk-Theorie und Assemblageforschung in Stadtforschung und Humangeographie beanspruchen für sich, das genau Hinsehen und die Materialität der Dinge stark zu machen. Sie positionieren sich damit gegen Forschung, die große Diskurse ins Zentrum rückt. Im Kommentar schlage ich vor, beide Positionen – die „Diskursphase“ und die „Materialismusphase“ – im Hinblick auf ihre politischen Einsätze zu kontextualisieren. Ich werfe die Frage auf, ob für diese Politiken die jeweiligen Ontologien tatsächlich notwendig sind, und schlage vor, sich stattdessen darauf zu besinnen, was die beste kritische Stadtforschung schon immer ausgemacht hat, nämlich genau hinzuschauen, die Dinge ernst zu nehmen und nicht schon alles vorher zu wissen.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".