Bibliographic record
Abstract
The colony that became Ontario arose almost spontaneously out of the confusion and uncertainty following the American Revolution, as a quickly chosen refuge for some 10,000 Loyalists who had to leave their former homes. After the War of 1812 settlers began to spread throughout the inter-lake peninsula that was to become southern Ontario and by the middle of the nineteenth century expansion had led to a diversifying agriculture and an increasingly open farming landscape that replaced a mature forest ecosystem. The scale of the change from forest to cropland profoundly affected what had been for many decades a rich environment for life forms, from large herbivores down to microscopic creatures. In Making Ontario David Wood shows that the most effective agent of change in the first century of Ontario's development was not the locomotive but settlers' attempts to change the forest into agricultural land. Wood traces the various threads that went into creating a successful farming colony while documenting the sacrifice of the forest ecosystem to the demands of progress, progress that prepared the ground for the railway. Making Ontario provides a detailed focus on environmental modification at a time of great changes. It is liberally illustrated with analytical maps based on archival research.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".