The Unconventional Boomtown: Updating the impact model to fit new spatial and temporal scales.
Bibliographic record
Abstract
The boomtown impact model developed by researchers in the 1970s implicitly assumes a spatially concentrated, finite resource will be extracted during a near-singular event (i.e. the boom), followed by a one-time bust. This model has been criticized for its lack of a realistic longitudinal or macro-level perspective beyond the boom-and-bust, and seems unlikely to transfer successfully to the context of modern hydrocarbon exploitation. Technological innovations have unlocked massive reservoirs of natural gas in many parts of the world that challenge the notion of a geographically concentrated supply that can be quickly exploited. While as prices have plunged and natural gas is poised as an attractive fuel source for some time to come, energy prices are likely to retain their characteristic volatility. Hydrocarbon rich regions and the communities in them are likely to experience repeated waves of mini-booms and mini-busts over the course of decades, a scenario for which the classic one-time boom/bust model may not be well equipped. This development pattern holds profound implications for the types of impacts experienced by residents and the ways in which communities can prepare for them. In this article, we seek to both better define the sets of assumptions that predicate the boomtown impact model and suggest updates to incorporate more macro-level economic concerns. We review the boomtown impact model for assumptions of rurality and isolation, land ownership and wealth retention, spatial and temporal concentration, and economic drivers and industry behavior. We compare these assumptions against the new reality of unconventional natural gas development, drawing from impacted communities in the Marcellus Shale of Pennsylvania that have experienced some of the new types of impacts. We further describe ways in which the boomtown model may be updated to include a more complex energy industry and implications for research and rural community development.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".