Bibliographic record
Abstract
Our problem is that we did not invent printing or the Bic pen, and that we’ll always end up at the bottom of the class thinking we could write the history of our continent with spears. Do you get my drift? And what is more, we have a bizarre accent that comes out in our writing, and people don’t care for it. Introduction: framing postcolonial France By the end of the nineteenth century, the British and the French shared the ambiguous prestige of wielding the most powerful empires and colonies. Their respective projects varied considerably in terms of geographic spheres of influence, and naturally so did the cultural strategies deployed. Any consideration of the legacy of these historical encounters must necessarily acknowledge these factors, particularly when one analyses the mutually constitutive nature of cross-cultural contact between these regions of the world. The shared historical experience needs to be foregrounded: ‘France and Africa share a common history, expressed jointly by the role France has played for centuries in Africa north and south of the Sahara, and by the more recent presence in the Hexagon of Africans who have, in turn, through their actions, their work, their thinking, had a concrete impact on the course of French history.’ In this regard, the French context is all the more complex given the concerted effort made by the colonial authorities in shaping policy through a civilizing mission determined to establish cultural prototypes in France overseas .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".