Bibliographic record
Abstract
The categories of e-commerce, like Business-to-Business (B2B), Business-to-Customers (B2C) and mobile Commerce (m-Commerce) makes use of core technologies different from each other (Salazar et al, 2003; Maxey, 2001) however, the common factor remains unchanged from the auditors' perception, i.e. risk and its potential to harm the integrity and accuracy of the data and decisions based on such data. E-commerce has begun in its varied facets. As an auditor, one may have to audit them. My effort is to identify the risks and show their impact on the assurance of the information system of any organization. AICPA/CICA (Cashell & Aldhizer III, 1999; Primoff, 1998) has jointly offered seal of assurance at web level and system level. The limitations of these certifications are equally important for an auditor as the objectives and the perceptions of these approval auditors are limited to their respective goals established by these accounting bodies. But, the role and functions of an auditor are beyond those of the assurance approval auditors. The organizational decision making processes are dependent on various segments of information bases whereas these assurance providers audit a limited amount related to their interest. This conceptual paper attempts to show why an auditor is expected to provide a much higher level of assurance to the organizational executives amidst a plethora of risks faced by their information and databases and accordingly a framework is provided for auditors to be implemented in the cyber entities under their review.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| 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".