An exploration and advancement of the concept of trust
Bibliographic record
Abstract
BACKGROUND: Trust is a concept used both in everyday language and in the scientific realm. An exploration of the conceptualizations of trust within the disciplines of nursing, medicine, psychology and sociology, revealed that trust is an ambiguous scientific concept. AIMS: In order to increase the pragmatic utility of the concept of trust for scientific application, further clarification and development of the concept was undertaken. METHODS: First, a concept analysis was conducted with the aim of clarifying the state of the science of discipline-specific conceptualizations of trust. The criterion-based method of concept analysis as described by Morse and colleagues was used (Morse et al. 1996a, 1996b, Morse 2000). This analytic process enabled the assessment of the scientific maturity of the concept of trust. The interdisciplinary concept of trust was found to be immature. Based on this level of maturity it was determined that in order to advance the concept of trust toward greater maturity, techniques of concept development using the literature as data were applied. In this process, questions were "asked of the data" (in this case, the selected disciplinary literatures) to identify the conceptual components of trust. RESULTS: The inquiry into the concept of trust led to the development of an expanded interdisciplinary conceptual definition by merging the most coherent commonalties from each discipline. CONCLUSIONS: The newly developed interdisciplinary conceptualization advances the concept toward maturity, that is, a more refined, pragmatic and higher-order concept. The refined concept of trust transcends the contextual boundaries of each discipline in a truly interdisciplinary scientific fashion.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".