Bibliographic record
Abstract
In November 2011 Science published a paper presenting research conducted by a team led by population geneticist Laurent Excoffier of the University of Montreal. This work repackaged in a genetics-inflected language a recurring tenet of settler colonial discourse, a point initially suggested by the apologists of the settler ‘transition’ of the nineteenth century and repeated since by their followers. The transition had transformed the anxious perception of rebarbarised Europeans living at the edge of civilisation. In Belich’s analysis, this was a momentous nineteenth-century transformation in the political imagination of emigration, a shift that radically altered the prospects of those who left the colonising cores for the settler peripheries of the ‘Angloworld’. 1 One result of this shift was that settler pioneers could be represented as inherently better humans — better than the peoples they had left behind and certainly better than the indigenous peoples they encountered. If this discourse was once framed in racial terms against indigenous peoples and other colonised populations (the settlers’ ‘Others’), or as a regenerative experience on the ‘frontier’ against those who had not moved there (the settlers’ other ‘Others’), in 2011 it was expressed with reference to a more efficient capacity to shape the genetic pool of future populations. It was in the present that the superiority of an historical experience could and should now be measured: Since their origin, human populations have colonized the whole planet, but the demographic processes governing range expansions are mostly unknown. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.032 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".