The <i>Syndicat Commercial du Mobilier et du matériel d’Enseignement</i> and the transnational trade of school artefacts <i>(Brazil and France in the late nineteenth and early twentieth centuries)</i>
Bibliographic record
Abstract
The article explores the role of the Syndicat du matériel et mobilier scolaire de l’enseignement in supplying French school materials to several countries, including Mexico, Canada, and Brazil, in order to demonstrate the profitability of a new industry, the school industry, and of a new type of trade, the transnational trade in school artefacts used as didactic resources. It is divided into four parts. The Introduction presents the context in which this commercial activity flourished, favoured by the developments of the second industrial revolution and the new educational guidelines associated with mass schooling and the method of “object lessons”. Next, it characterises the enterprise in Brazil, from the presentation of commercial agents operating in the states of Bahia, Rio de Janeiro, and São Paulo to the identification of the strategies mobilised for the sale and importation from France of school artefacts, conceived as merchandise. In the third part the lens is reversed, and the objective is to examine the ways in which purchases were made by the public education administration in São Paulo. As a final comment, the article reaffirms the connections between the values of capitalist society, consumption practices and the material elementary schooling universe between the late nineteenth and early twentieth centuries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".