La croissance démographique entre science et science-fiction :
Bibliographic record
Abstract
RésuméDepuis 1950, les Nations Unies publient des projections de population. Ces projections suscitent depuis cette date de nombreuses inquiétudes aussi bien dans les organisations internationales, que dans l’opinion publique. La croissance de la population annoncée par les projections parait pour beaucoup annonciatrice de famines, dégradations des ressources, conflits. Les auteurs de science-fiction notamment anglo-saxonne se nourrissent de ces peurs pour leurs romans. La croissance démographique se retrouve au centre de l’intrigue de Make room! Make room!, Les Monades urbaines, Tous à Zanzibar… La science-fiction imagine ce que la science démographique ne prévoit pas : des modes de vie et de production, des organisations sociales contraintes par un « surpeuplement ». Si la science-fiction ne se révèle pas forcément comme un moyen de prédire l’avenir, elle est, entre 1950 et 1980, un bon révélateur des peurs et des angoisses liées à la croissance démographique. Mots-clés : Démographie, science-fiction, projection, croissance démographique AbstractThe United Nations has been publishing population projections since 1958. Since then, these projections have given rise to a great deal of concern among both international organisations and public opinion. The population growth predicted by the projections seems to many to herald famine, resource degradation and conflict. Science fiction writers, particularly in the English-speaking world, feed these fears into their novels. Population growth is central to the plot of Make Room! Make room!, Les Monades urbaines, Tous à Zanzibar... Science fiction imagines what demographic science does not: ways of living and producing, social organisations constrained by ‘overpopulation’. While science fiction was not necessarily a means of predicting the future, between 1950 and 1980 it did reveal the fears and anxieties associated with demographic growth. Keywords: Demography, Science fiction, projection, population growth
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".