A Model for Total Cost Determination in Open-Source Software Ownership: Case of Kenyan Universities’ Learning Management System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The adoption of open-source products is slowly increasing; the increase, however, is slower than expected, considering that most open-source products are freely available. Researchers and scholars have attributed the adoption levels to, among other things, a lack of know-how of the total cost of ownership of the open-source software. Thus, it is crucial for the cost of owning the software to be developed. While an ongoing endeavor to develop a model to determine the total cost of ownership of open-source software, the models have proved to be less accurate and do not capture essential elements. Moreover, there has been a rising call for organizations to adopt open-source software to lower the software costs incurred on proprietary software. If the cost of owning open-source software were known, it would be beneficial as several organizations and institutions could adopt it readily. The data was collected from Universities in Kiambu and Embu Counties. Linear regression analysis was done to help develop the model, and a mathematical model was developed. The proposed model was: total cost of open-source software ownership = direct + +indirect + hidden costs. To validate the model, it was subjected to expert validation. The model will be an outstanding contribution to information technology as it will make it possible to come up with the total cost of owning open-source software.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.012 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it