Bibliographic record
Abstract
It is often said that we learn from our mistakes. This may be true for individuals, but collectively, humanity repeatedly continues to exhibit collective amnesia and, by ignoring past events – willfully or not – the same mistakes continue to be made. A case in point may be our continued dependence on and consumption of fossil fuels, most notably oil and natural gas. Since the discoveries of oil fields in the upper mid-west of the United States and in southern Ontario, the world economy has become truly dependent on this black gold, led, of course, by a handful of oil-thirsty industrialized nations. Yet most western consumers appear to have little concern about the available resources and how limited these resources are – much like in the early days of the American and Canadian oil boom. Recoverable oil reserves are limited – certainly the end of cheap oil is in sight, if not here already. This may not necessarily imply the end of civilization as we know it, but surely the decline in world oil production will necessitate changes in our way of life and thinking. Perhaps most surprising is the fact that many smaller communities have experienced their local “peak oil” and gone from boom to bust – yet, many people, including most politicians, continue to be optimistic and hope for some magical solution to our problems. As argued in this report, what has happened on smaller scales to the towns of Petrolia, PA, ON, TX, CA, and too many other communities in the original heartlands of oil exploration, is bound to replay on the world stage. This time around, however, there may not be an easy way out.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".