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 â granularityâ . Additionally, organizations are emerging which are acting as learning resource brokerages or networks, such as UNIVERSAL in Europe and AUShareNet in Australia. There are insights and possible future business relationships for OnDisC to be realized in all of the above educational content delivery organizations. A universal issue among public LO repositories and LSG is how to acquire funding/revenue to sustain the organization beyond initial project status. Most of them are following a sponsorship model where operating and development funds are received from government and/or other supporting organizations and individual educators provide content free. Their business/revenue model follows from a consideration that they are providing a public good which can/must be supported by third parties. OnDisC may be able to operate under a similar business model for similar public goods markets. Additionally, OnDisC may be able to provide LO content to commercial content developers either directly, or through future online educational material brokerage sites/marketplaces. A valuable tool for helping to formulate business and revenue models is a value chain assessment in which all significant value added processes or functions and determined and assigned to the different players or organizations involved in the value chain. Once value added assessments are made, appropriate revenue streams can be modeled. A relevant and useful value chain assessment to consider for OnDisCâ s situation is that of the traditional publisher-library book/journal distribution system. A significant source of risk for the providers of digital content to a store or repository is the high cost associated with digitizing the material into a format suitable for distribution and use. A possible compromise between risk and service is to provide just-in-time digitization for material that has been chosen as desirable by an end user.
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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 | 0.012 | 0.033 |
| 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 | 0.009 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".