Bibliographic record
Abstract
New technologies that influence how information is created and shared and how people connect and socialize hold promise for adoption in education.Much like the idea of a book necessitated the development of the library or the idea of structured curriculum and domains of knowledge produced classrooms, the idea of the Internet -distributed, social, networked -influences the structure of education, teaching, and learning.Educators and researchers face a challenge in determining how the existing education system will be influenced and the new roles that will be expected of learners, teachers, and administrators.Information-centric fields such as journalism have struggled with the new democracy of information creation for over a decade.The music industry continues to grapple with access issues and the "unbundling of the album" initiated by Napster and firmly entrenched by iTunes.Telephone companies face an uncertain future as Skype, Google Voice, and other web-based communication services increase in popularity.Essentially, the Internet has remade how society creates and shares content and how people communicate and interact.The implications for education are significant.Educators have explored the role of the Internet as a research and learning tool for several decades.In the late 1990s, social network services (e.g., Friendster) and easy publishing tools (such as blogs) increased the ability for anyone with an Internet connection to both publish and engage in online conversations.Since that time, we've experienced a decade of amazing innovation in social networking sites (Facebook, Twitter), in openness movements (open source, open access), in mobile technologies (mobile phones, iPads), in the growth of broadband, in gaming, in multimedia (YouTube, podcasts), and in new tools that blend the physical and virtual worlds (location-based services such as Foursquare and Groupon, augmented reality, "internet of things").
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.073 | 0.067 |
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".