The Re-designing of Business Education through Web Tools: From Universities Web-radio to On-line Magazines
Bibliographic record
Abstract
Traditional teaching methods should be redesigned and renewed by consulting all those involved in the learning process: students, teachers, institutions, companies, in order to create a network whose aim is to exchange and transfer knowledge. The criteria used to choose a distance learning course program, should also be applied to building the right network to reach pre-determined goals. In both cases, we have a classroom- real or virtual- and it is only when we step outside the usual learning environment, that we can increase learning opportunities, by stimulating and motivating learners in a new way. Stepping out from a traditional classroom environment results in an open mind regarding the world of work , especially in the field of Business Education. New ,innovative and creative web 2.0 tools, such as university web-radios and on-line magazines could offer this opportunity by modifying teaching methods as could being part of the above-mentioned network . Moreover, both tools are user-friendly, cheap and quite simple to put into practice and manage. As there was little data available regarding Italian university web-radios and national academic magazines, we decided to carry out our own research. Little research had been previously carried out in this area.In this paper, we refer to WayOut a pilot project aimed at developing an entrepreneurial mindset by, engaging students in an interesting learning environment in order to make them capable of managing their life project, of reaching specific goals and subsequently of being able to take advantage of opportunities, which arise. Our aim is to encourage interaction between universities and businesses by removing the barriers between the internal and the external environment, thus making reciprocal knowledge transfer easier: from the company to the university and from university to the company.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".