Bibliographic record
Abstract
Social scientists have suggested that more careful theoretical work on the nature of trust is required to satisfactorily carry out their research. At the same time, recent work in philosophy on the topic of trust incorporates very little of the existing empirical work that has been completed and might inform the theory. In this article, I add my voice to the chorus calling for greater transdisciplinary work on the topic of trust, and I aim to contribute to this work by proposing a conceptual infrastructure that can help to clarify and substantiate the theoretical foundations of existing empirical work on the topic of trust in political leaders. This infrastructure will recommend a typology of theories of trust that includes entrusting theories, which focus on what is entrusted, trusting theories, which focus on the values and dispositions of the truster, and trustworthy theories, which focus on the trustworthiness of the trustee. This conceptual infrastructure will be theoretically useful, providing a language in which to understand and articulate the nature of trust and trustworthiness as well as normative matters having to do with the relationship between trust and trustworthiness (i.e., when should a political leader be trusted?). It will also be empirically useful, providing a recommended method to determine a set of concepts that can be deployed in empirical work on the presence or absence, and evolving dynamics, of trust and trustworthiness (i.e., when is a political leader trusted?).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".