Environmental Scan of Pricing Models for Online Content: Report II: Business Models for Object Repositories
Bibliographic record
Abstract
This report investigates Canadian and other initiatives in developing e-content stores or repositories with special interest paid to their business and revenue models for background in determining a suitable sustainable business/revenue model for the OnDisC Alliance. There is significant activity worldwide in the research and development of repositories of Learning Objects (LO)-- modular chunks of content that are combined and reused to form larger aggregations of education content such as lesson, units, and courses. The rationale for developing repositories of LOs is to reduce the significant cost of developing and customizing educational material. There is activity in developing LO repositories in both the public sector and the private sector. MERLOT is a large public and free LO repository co-operative. Some private firms developing LO repositories and the tools to create and use them include NetG, SmartForce and LearningWay. In addition to LO repositories there are many Learning Resource Gateways (LRG) which offer both free and non-free educational material of many levels of object “granularity”. Additionally,
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".