Bibliographic record
Abstract
The term Consumerisation of IT (CoIT) has been buzzing for a while in the IT world. The best-known form of this trend is Bring Your Own Device (BYOD), a term even your mother has probably heard by now. The popularity of BYOD in the UK is not as clear as you might think. The researchers of Strategy Analytics saw a decrease of 15% in BYOD in Western Europe in the first quarter of 2013, compared to the first quarter of 2012. 1 The serious concerns of IT managers in this domain seem to have been heard by the company decision makers . But now that this threat seems to be declining, the next buzzword has made its appearance: Bring Your Own Software (BYOS). Is that also a dangerous trend? And what are the expected advantages of BYOS? The term Consumerisation of IT (CoIT) has been buzzing for a while in the IT world, with Bring Your Own Device (BYOD) its best-known manifestation. Yet even as BYOD seems to be fading as a threat, Bring Your Own Software (BYOS) is raising its head as the next potentially dangerous trend. So what are the expected advantages of BYOS? And do they warrant the possible risks? Daniëlle van Leeuwen of G Data Software explains.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.076 | 0.063 |
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".