Putting Port Royal on the Map: Jean de Labat's Early-18th-Century Cartographic Construction of Port Royal
Bibliographic record
Abstract
Abstract: Les cartes géographiques sont autant des images de fantaisies impériales que des sources de renseignements géographiques pratiques. Le travail de Jean de Labat, un ingénieur militaire français à Port-Royal, offre un cas d'espèce des expériences cartographiques impériales dans le nord-est de l'Amérique du Nord. Une lecture critique élargie de deux de ses cartes révèle que les réalités sur le terrain se mêlaient aux fantaisies impériales dans la construction d'une version de la vie à Port-Royal qui profitait à Labat et à d'autres fonctionnaires français. C'est pour ces raisons que le travail de Labat fut publié plus tard dans l'édition de 1727 des Cartes marines . Abstract: Maps are as much images of imperial imaginings as they are sources of practical geographic information. The work of Jean de Labat, a French military engineer at Port Royal, offers a case study of cartographic experimentation with empire in northeastern North America. An expanded critical cartographic reading of two of his maps reveals that on-the-ground realities mingled with imperial imaginings to construct a version of life at Port Royal that benefitted Labat and other French officials. It was for these reasons that Labat's work was later published in the 1727 edition of the Cartes marines .
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".