La confiance et le rapport aux normes : le problème de la méfiance face à la différence
Bibliographic record
Abstract
Nous proposons dans ce texte une hypothèse explicative de la méfiance plus grande observée entre des individus différents les uns des autres sur un aspect identitaire jugé pertinent par ceux-ci. Nous débutons par présenter une esquisse de définition de la confiance et nous plaçons les normes de rationalité et de moralité au centre de cette définition. Nous faisons confiance à autrui pour qu’il respecte certaines normes explicites ou implicites. Cette définition nous permet d’éviter deux écueils : celui de considérer qu’il est immoral de ne pas faire confiance à autrui, et celui de considérer qu’il est irrationnel de ne pas faire confiance à autrui. Nous ne pouvons donc pas critiquer la tendance à ne pas faire confiance à des individus différents d’un pont de vue moral ou rationnel. Nous terminons en proposant une hypothèse permettant d’expliquer comment peut émerger la méfiance entre individus différents.In this paper, I offer a hypothesis explaining how and why lower trust levels are observed between people who consider themselves as different on a relevant identity variable. I begin by clarifying the central aspects of a definition of trust built around the idea that trusting someone is basically trusting to follow some norms. These norms can be implicit or explicit; they can be norms of rationality and norms of morality. We trust people to act rationally or morally, which is different from trusting rationally or morally. I argue that defining trust as rational or moral is a mistake and so the lower trust level towards different people cannot be criticized from a rational or moral point of view. I conclude the paper by offering a possible explanation of this lower trust level based on my proposed definition.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".