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
This study provides an application framework toward measures to prevent/control identity theft in conjunction with sources. It also identifies the impact of overall protection of identity theft on consumer trust, the cost of products/services, and operational performance, all of which in turn contribute to a purchase intention using E-commerce (EC). For the first objective, this study proposes a matrix of sources and measures to prevent and control identity theft. From this matrix, using knowledge from a literature review and judgment based on plausibility, the authors identify global laws, controls placed on organizations, publications to develop awareness, technical management, managerial policy, risk management tools, data management, and control over employees are the potential measuring items to prevent identity theft related to EC. A case study in banking sector through a qualitative approach was conducted to verify the proposed relations, constructs, and measuring items. For the second objective, this research paper conceptualizes a model based on literature review and validates that based on the case study in the financial sector. The model reflects the effects of preventing and controlling identity theft on the costs of products/services, operational performance, and customers’ perception of trust, which would lead to purchase intention in EC.
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 | 0.001 | 0.011 |
| Open science | 0.001 | 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 it