Boom & Bust. Local strategy for big events. A community survival guide to turbulent times
Bibliographic record
Abstract
Boom and Bust: Local strategy for big events is the result of a collective effort at the University of Alberta to better understand the dramatic ups and downs which too often characterize western Canadian communities. From the Canadian analysis stems this book, which can be helpful in any community experiencing radical ups and downs, any community worried about its future. It offers community leaders, politicians, administrators, academics, students, and all active citizens helpful techniques to analyze the current state of their own community, understand how it got where it is today, and ultimately, identify possible ways forward. We encourage analysis of historical paths and policy contexts to better understand what strategies might work (or not) in a community. The authors encourage readers to learn from local histories, a broad range of tested theories, and the experiences of other communities to develop a context-sensitive strategy of asset building, while at the same time taking on an informed understanding of what assets and resources could support long-term development planning for their communities. They demonstrate that assets become such within a context and within a narrative, forming a story about the past, present, and future of the community. By showing the importance of reinvention and the dangers of rigid identity, the authors call on communities to re-evaluate their assets and their dependencies, and ultimately to reintroduce long-term perspectives within governance.
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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".