Bibliographic record
Abstract
About this time last year I gave a presentation on Peak Oil at the Atlantic Planners’ Institute conference in St. John’s, Newfoundland. For most of the people in the room, it seemed to be the first introduction to the issue. Then, this summer, I gave another presentation in Vancouver. I didn’t know how dialed-in everyone was, so the first thing I asked was, “Show of hands … when I say ‘Peak Oil ’ or ‘Hubbert Peak, ’ who here has no idea what I’m talking about? ” And the entire room was like, “well, duh. ” I might as well have asked them if they’d heard the Earth goes around the Sun. So that’s pretty remarkable, how fast this issue has become common knowledge. In eight months I went from being the bearer of bad news, to accidentally insulting everyone’s intelligence by suggesting it was news at all. The modern planning profession came into being just over a hundred years ago, at almost exactly coincident with the dawn of the petroleum era. By extension, North American planning has lived its entire life so far with a certain set of background assumptions. The key assumption is that energy is cheap and abundant and there’s more of it every year. And when you get right down to it, planning has been about dealing with the effect of this. Industrial cities, urban growth, urban sprawl, traffic congestion—these are all basically side effects of cheap energy. When that cheap energy is gone, the assumptions and the principles of planning are going to be turned on their ear. So that’s what this show is about. 21. The Laws of Thermodynamics
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.538 | 0.250 |
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".