Beyond the dominant narratives: Identifying Indigenous and African individuals in Quebec St-Lawrence Valley colonial cemeteries by three-dimensional morphometrical analysis of the temporal bone
Bibliographic record
Abstract
In Quebec, written accounts of free or enslaved Indigenous and African individuals during colonial times are few and limited. Three-dimensional geometric morphometric methods applied on 214 temporal bones individuals are used to explore intra-cemetery population variation throughout three centuries (1683–1878), to identify plausible non-European or admixed individuals. First, Principal Component Analysis (PCA) was generated from populations originated from Africa, North-America (Indigenous) and Europe where colonial cemeteries are projected to assess their degree of overlap with the reference groups. Second, Discriminant Function Analysis (DFA) based on typicality probabilities also allowed to assign unknown individuals to the three reference groups. Our results highlighted discrepancy between PCA and DFA. With the PCA, Quebec colonial groups overlap with the Europeans except for three individuals. For the DFA, 64 % (n = 136) were classified as typical to one group and 36 % as atypical (n = 77). Most were European, (82 %; n = 112), followed by those as Indigenous (7 %; n = 10), intermediate between Africans and Indigenous (6 %; n = 8) and Africans (5 %; n = 7). Compared with previous research, one individual from the first Protestant cemeteries of St-Matthew in Quebec could be assessed as plausible African or admixed origin. Two others need further analysis: one from St-Matthew and one from the first Catholic cemetery of Notre-Dame in Montreal. Finally, in response to dominant narratives that focused mainly on Euro-colonists, this research provided for the first time a new snapshot of several colonial population diversity in Quebec, underlining the presence of marginalized groups -especially those of African origin, who are absent from the archaeological reports under study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".