Bibliographic record
Abstract
DeVry University has expanded over the years with five distinct colleges, more than ninety locations in the US, Canada, and Brazil.DeVry offers traditional faceto-face courses in the classroom, online courses and hybrid or blended courses, and uses technology to enhance the curriculum.The expansion presumably increased the number of students as well as DeVry faculty and staff.Each student, instructor, manager, and staff member is different, yet each is striving towards the same goal: success.It is important to create a unique learning environment regardless of the culture or language.Technology plays a key role in this objective, and with the advancements in technology, it is important for our university to aim to use the newest and most efficient technology in order to encourage collaboration between learners, instructors and others in the academic community.Being more efficient is important, but being cost effective is equally important.Cloud computing is a technology that can be used to streamline the learning process and infrastructure, making it easier for students, teachers and administrators to strive towards academic success.It is the technical means by which everything can be delivered as a service over the internet, accessible from any device, any place, anytime.By providing access to education and the academic community anytime and anywhere, DeVry can emerge as a leader in the academic world.DeVry is made up of many voices but is one university, and cloud computing could be the tool used to unite our vision, objectives, and values for many different audiences.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.012 |
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".