University Land Grabs: Indigenous Dispossession and the Universities of Toronto and Manitoba
Bibliographic record
Abstract
Whether leased or sold, Indigenous land provided the endowment capital for new universities in the nineteenth century. Many institutions of higher learning – including the universities of Toronto and Manitoba – began as large-scale landowners. By 1828, Toronto’s university still held more than two hundred thousand acres of land, an area larger than the present-day city of Toronto. The extent of university landholding in settler societies, however, has often been missed because the land parcels assigned to universities were both larger than, and distinct from, their campuses. This article accordingly examines how landholding undergirded Canadian universities’ development in the nineteenth century, taking the University of Toronto and the University of Manitoba as its focus. It argues that land was the essential ingredient in university building in both Ontario and Manitoba, linking these new universities’ establishment and subsequent wealth to Indigenous dispossession. Using Indigenous land to finance higher education was not unique to these universities nor to Canada. Yet, across settler societies, university landholding made institutions of higher education the beneficiaries of Indigenous removal and agents of colonization. In addition, once in operation, these new universities would also produce knowledge about land and its cultivation. Inspired by the growing field of European agricultural science, Canadian universities with land endowments professionalized the study of branches of knowledge like agriculture and engineering, displacing Indigenous ways of being and thinking about land. The effect of this knowledge valuation is still felt today. The products of university agricultural research – from nitrogen fertilizers to hybridized corn – profoundly transformed landscapes and altered local ecologies in line with settler knowledge systems and desires.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".