The Day After Tomorrow: Transition Management, Spatial Planning & the Low Carbon Economy
Bibliographic record
Abstract
Many of the societal challenges that current spatial planning practice claims to be addressing (climate change, peak oil, obesity, aging society etc) encompass issues and timescales that lie beyond the traditional scope planning policy (Campbell 2006). The example of achieving a low carbon economy typifies this in that it demands a process of society-wide transition, involving steering a wide range of factors (markets, infrastructure, governance, individual behaviour etc). Such a process offers a challenge to traditional approaches to planning as they cannot be guided by a fixed blueprint, given the timescales involved (up to 50 years) and an enhanced level of uncertainty, social resistance, lack of control over implementation and a danger of ‘policy lock in’ (Kemp et al 2007). One approach to responding to these challenges is the concept of transition management which has emerged from studies of science, technology and innovation (Geels 2002, Markard et al 2012). Although not without criticism, this perspective attempts to uncertainty and complexity encompassing long term visions that integrates multi-level, multi-actor and multi-domain perspectives (Rotmans et al 2001). Given its origins, research on transition management has tended to neglect spatial contexts (Coenen et al 2012) and, related to this, it’s relationship with spatial planning is poorly understood. Using the example of the low carbon transition, this paper will review the relationships between the concepts, methodologies and goals of transition management and spatial planning to explore whether a closer integration of the two fields offers benefits to achieving the long term challenges facing society.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".