From Big Data to Dirt Research: Mapping Canadian Energy Transitions in City, Field, and Forest
Bibliographic record
Abstract
From prairie wheat kings to log-waltzing timber drivers, Canadians evoke a sense of working with the natural world. For most of Canadian history, its primary sector operated in what economist E. A. Wrigley called the “solar regime” of energy history, limited by the biomass that plants and animals could convert from the sun’s energy. The transition from biomass to fossil fuels was universal, but in Canada it was surprisingly slow, and historians know relatively little about it. From careful map analysis in historical Geographic Information Systems (HGIS) to Deep Learning Models in ArcGIS Pro, we use a range of digital methods to mine data and examine these transitions in UPEI’s GeoREACH Lab (for Geospatial Research in Atlantic Canadian History). We use HGIS for everything from automated polygon recognition to online participatory mapping, and combined with traditional historical methods such as oral interviews and census data development, our students have helped to digitize maps and manuscripts with a focus on the period of Canada’s largest energy transition (circa 1870-1970).
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".