Bibliographic record
Abstract
The concepts of open educational resource and virtual community of practice stem from the need to create a new mode of learning and knowledge creation platforms that complement the classroom environment. This paper aims to find how to take advantage of outcome-based learning to advance development and mastery of competencies, skills, and professional practice as a mediating product in service of innovation. The case of consideration in this paper is an open educational resource (www.g9toengineering.com) developed by the author in 2007 and continuously maintained and updated by students from an undergraduate mechatronics engineering class as an online portal to enable knowledge sharing and transfer basing on the framework of “acquire-reflect-share-apply”. This reflection on challenges of knowledge creation shows in particular how faculty and students collaborate to create their own learning content that is part of their course, then openly share and further develop at no cost and free from licensing obstacles. Results show that students are positive with their own understanding and knowledge building with no clear distinction between the types of knowledge. Some of the challenges of implementation practice are shared in this paper.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".