Bibliographic record
Abstract
The revolution in information technology and the use of the internet changed the lifestyle of people. A major change was in the way of shopping. Companies started to offer their products online using social network s and people started to buy from the internet. Using social network has many benefits to the users starting from exploring a large variety of products to the very first way of ordering and the availability of the products 24 hours a day. One of the main problems that is found in using a social network is trusting the using social network social network s. the student concern about trusting to buy from the using social network social network s. Trust is a major concern for the merchant too; his concern is how to gain the student trust and to keep it. Many factors play a major role in acquiring the student trust in the online market. These factors rely on the social network characteristics such as design, interactivity and age and other factors vary from the social network quality, service quality, security policy of the social network, the privacy policy, the guarantee offered, the satisfaction of the user, the ease of use, the risk aversion and the culture factors. This study introduces the trusting affecting factors mentioned above and their effect on the trustworthiness factors (ability, benevolence, and integrity) a trust model has been built to show the relation between these factors and the trustworthiness factors.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".