Bibliographic record
Abstract
Abstract In this address, I examine the lexical, geographic, temporal and philosophical origins of two key concepts in modern political thought: colonies and statistics. Beginning with the Latin word colonia, I argue that the modern ideology of settler colonialism is anchored in the claim of “improvement” of both people and land via agrarian labour in John Locke's labour theory of property in seventeenth-century America, through which he sought to provide an ideological justification for both the assimilation and dispossession of Indigenous peoples. This same ideology of colonialism was turned inward a century later by Sir John Sinclair to justify domestic colonies on “waste” land in Scotland—specifically Caithness (the county within which my own grandparents were tenant farmers). Domestic colonialism understood as “improvement” of people (the “idle” poor and mentally ill and disabled) through engagement in agrarian labour on waste land inside explicitly named colonies within the borders of one's own country was first championed not only by Sinclair but also his famous correspondent, Jeremy Bentham, in England. Sinclair simultaneously coined the word statistics and was the first to use it in the English language. He defined it as the scientific gathering of mass survey data to shape state policies. Bentham embraced statistics as well. In both cases, statistics were developed and deployed to support their domestic colony schemes by creating a benchmark and roadmap for the improvement of people and land as well as a tool to measure the colony's capacity to achieve both over time. I conclude that settler colonialism along with the intertwined origins of domestic colonies and statistics have important implications for the study of political science in Canada, the history of colonialism as distinct from imperialism in modern political thought and the role played by intersecting colonialisms in the Canadian polity.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".