Unsettling planning theory
Bibliographic record
Abstract
Recent political developments in many parts of the world seem likely to exacerbate rather than ameliorate the planetary-scale challenges of social polarization, inequality and environmental change societies face. In this unconventional multi-authored essay, we therefore seek to explore some of the ways in which planning theory might respond to the deeply unsettling times we live in. Taking the multiple, suggestive possibilities of the theme of unsettlement as a starting point, we aim to create space for reflection and debate about the state of the discipline and practice of planning theory, questioning what it means to produce knowledge capable of acting on the world today. Drawing on exchanges at a workshop attended by a group of emerging scholars in Portland, Oregon in late 2016, the essay begins with an introduction section exploring the contemporary resonances of ‘unsettling’ in, of and for planning theory. This is followed by four, individually authored responses which each connect the idea of unsettlement to key challenges and possible future directions. We end by calling for a reflective practice of theorizing that accepts unsettlement but seeks to act knowingly and compassionately on the uneven terrain that it creates.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.090 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".