Bibliographic record
Abstract
Electronic commerce or e-commerce can be briefly defined as a financial transaction or commercial information between two parties based on data transmitted over communication networks (Soriano & Ponce, 2002). It relies upon users’ interventions to initiate a transaction and select the main steps of the process. Users’ actions stem from a succession of virtual decisions. Indeed, when shopping with a virtual catalog, customers can select products which meet their needs, tastes, and respect their price range. Such decisions consistently require the users’ input, thus costing them both time and money. These costs are even more exorbitant when a search is launched for an order that includes a variety of products from different sources which have different characteristics (price range, delivery dates, etc.). When transactions involve users who are moving or take place over mobile networks, this is referred to as mobile electronic commerce, a specific type of e-commerce. Mobile electronic commerce (or m-commerce) refers to an ability to carry out wireless commercial transactions using mobile applications within mobile devices, such as mobile phones and personal digital assistants (PDAs). It is generally defined as the set of transactions or processes which can be carried out over a wireless mobile network. According to this definition, m-commerce constitutes a subset of all electronic commercial transactions (electronic commerce or e-commerce) from business to consumer (B2C) or business to business (B2B). Thus, short personal messages such as those from SMS (short messaging system) sent between two individuals do not fall into the category of m-commerce, whereas messages from a service provider to a salesperson or a consumer, or vice versa, do fit this very definition. M-commerce appears as an emerging manifestation of Internet electronic commerce which meshes together concepts such as the Internet, mobile computing, and wireless telecommunications in order to provide an array of sophisticated services (m-services) to mobile users (Varshney, Vetter, & Kalakota, 2000; Veijalainen, Terziyan, & Tirri, 2003). E-commerce includes an initial step where consumers search for a product they wish to purchase by virtually visiting several merchants. Once the product is found, negotiations can take place between the customer and the merchant (electronic negotiation or e-negotiation) (Paurobally, Turner, & Jennings, 2003). If an agreement is reached, the next step is the payment phase. At each step of the process, some problems arise, such as transaction security, confidence in the payment protocol, bandwidth limitations, quality of service, shipping delays, and so forth (Younas, Chao, & Anane, 2003; Zhang, Yuan, & Archer, 2002). The peak withdrawal periods have always presented a major challenge for certain types of distributed applications. The advent of m-commerce further highlights this problem. Indeed, in spite of rather optimistic predictions, m-commerce is plagued by several handicaps which hinder its commercial development, security being the main one.
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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".