Improving the Future of \nLearning Through Enhanced Collaboration Methods and Platforms
Bibliographic record
Abstract
In a rapidly changing world, to maintain relevancy in the face of competition, many businesses and sectors are re-examining how they develop the best product offering for their customers. This is especially true of the education system. Social trends and on-line connectivity are changing how people explore and experience the world, learn, interact, and work. \n \nInspired by how these changes are affecting the Informal Science Learning sector and it’s needing to evolve in front of looming disruption, this Major Research Project examines the implications of critical trends and how they can be harnessed to co-create new shared value not only in this sector, but more broadly and globally. \n \nThe research starts with an examination of the importance, nature, complexity and challenges of creating public offerings (informal learning based content) in the typical science centre. Using the Ontario Science Centre as a case study, critical trends that might lead to business model change were examined. \n \nCritical trends included the globally growing network of science centres, the success of involving customers in co-creation and the continuing advancement of remote collaboration technologies and capabilities. \nIn order to unpack the current state and potential of remote collaboration, expert interview research was conducted focusing on collaborative tools and methodologies and best practices. Further literature research also noted the growth of on-line experience platforms, and a convergence of co-creation, learning and work. \n \nMost importantly, the research concludes that technological advancements and successes in remote co-creation suggest an emergent model for the improved future of learning, work, productivity and the potential for the crowd to democratically self-assemble and engage in design and problem solving.
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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".