Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Trust is a major factor influencing the cohesiveness among virtual team members. While recent research in the fields of information systems and management has examined this construct, there are no existing instruments that measure all the different bases of trust. Drawing on the literature, three different bases of trust applicable to virtual teams have been identified: personality-based, institutional-based, and cognitive trust, with cognitive trust further subdivided into three dimensions: stereotyping, unit grouping, and reputation categorization. This paper reports on the development of an instrument to capture these three bases of trust. Using exploratory, and thereafter, confirmatory factor analysis, the instrument is validated, and the psychometric properties of the construct(s) are verified in the context of U.S.-Canadian student virtual teams engaged in systems development projects. In addition to confirming the conceptual bases of trust, the instrument validation process found that stereotyping in virtual teams can be of three distinct types: message-based, physical appearance/behavior-based, and technology-based. The development and validation of this instrument should enable future researchers to measure virtual team trust in a broad range of technology and team configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it