Finding the future in policy discourse: an analysis of city resilience plans
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Managing future uncertainty is the essence of planning. How planners conceptualize the future therefore has important practical and normative implications as contemporary decisions have long-term impacts that may be irreversible and distribute costs and benefits across society. A discourse analysis of strategies prepared under the 100 Resilient Cities programme reveals that while they are ostensibly forward-looking and cognizant of uncertainty, most presume a knowable future (epistemic certainty) and focus on well-understood or recently experienced risks. Few acknowledge the future’s inherent unknowability (ontic uncertainty). Those that do emphasize community self-help; others describe top-down, government-led initiatives. Most strategies also present an image of societal consensus, downplaying the potential for legitimate disagreement over means and ends (discursive uncertainty). These findings suggest that new conceptualizations of future uncertainty have had limited impacts on planning 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it