Sync&Share North Rhine-Westphalia
Bibliographic record
Abstract
Raimund Vogl is the project leader of a large-scale project which aims to introduce a university-based cloud storage services to major German universities. The protagonist needs to prepare himself for a meeting with the project sponsors, the Ministry of Science and Research, to convince them to go ahead with the project. The scenario is based on a real case and shows real challenges. The university-based scenario helps students to better put themselves in the context of the case. Also, the case serves to teach the basic principles of cloud computing. The main challenge faced by the protagonist is to come up with a plan for user adoption. Accordingly, several technology-related theories can be used. In addition, this goes along with the need of Sync&Share NRW to be perceived as a trustworthy provider. The case helps to understand the concept of trust, the relationship between trust and cloud computing acceptance and ways to gain trust in the context of cloud computing. Moreover, there are two additional challenges. First, the demand for support needs to be solved with only very limited human resources. Second, illegal file-sharing needs to be strictly prevented in order not to suffer from a loss of reputation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.631 | 0.477 |
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".