Trust, trustworthiness, and relationships: ontological reflections on public trust in science
Bibliographic record
Abstract
There is much social scientific research dedicated to measuring and studying public trust. I examine the ways in which the notion of trust is implicitly conceptualised in such studies. I argue that there is a common ontological foundation in most research on the topic: trust is viewed as a phenomenon that is an attribute of individuals, intrapsychic, directed toward specific targets, and that is quantitative and measurable. I criticise this conceptualisation of trust and argue that it: (1) fails to consider the trustworthiness of individuals and institutions, (2) fails to recognise trust as a relational phenomenon and overlooks historical and material conditions that characterise relationships between people and institutions, and (3) lends itself to bureaucratic manipulation of publics rather than fostering authentic relationships of trust. I conclude that studies on trust need to be situated in larger frameworks that attend to the trustworthiness of actors and to relationships between them.
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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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.109 |
| Scholarly communication | 0.022 | 0.042 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".