Athabasca University CENTRE FOR DISTANCE EDUCATION Online Software Evaluation Report TITLE: OS Software: an alternative to costly Learning Management Systems
Bibliographic record
Abstract
This is the first in a series of two reports discussing the use of open source software (OSS) and free software (FS) in online education as an alternative to expensive proprietary software. It details the steps taken in a Canadian community college to download and install the Linux Operating System in order to support an OSS/FS learning management system (LMS). Background The Woodstock College campus of the New Brunswick Community College system is a small location with an on-site seat capacity of 300 students. The College has approximately 250 computers (staff and student labs), connected through two servers (administration and student) operating in a Windows environment. Technical support is provided by two individuals responsible for all aspects of the Information Technology (IT) infrastructure, including software management. The IT staff is called upon to provide technical support for specialized software used by instructors for industry-specific applications. The College’s operating budget supports the overall IT infrastructure, including the resources and professional development required by instructors in the use of the new learning technologies. As with many educational institutions, this budget is being increasingly stretched by the costs of proprietary
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".