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 Grand Emporium of the West project created and investigated new forms of m-Learning, based on matters of historical significance, delivered through mobile technologies, and focused on the emerging affordances of the medium. Development of this project began in March 2012 -- with planning, initial user tests, and early prototypes –triggered by this NEH "We the People Grant" investment. The grant supported the research team's work in this area, but it also led to the development of new material, tools, and approaches for secondary schools to teach and learn about history, through apps (available for both Apple and Android tablet computers). The grant not only generated a significant burst of new multimedia material – integrated into the tablet apps but also repurposed into related smartphone apps, with the media, when appropriate, geolocated at the primary research site, the Fort Vancouver National Historic Site in Vancouver, WA, but it also served as a major catalyst for additional funding, larger collaborations with the National Park Service, educational advancements, more app development, and nationally and internationally distributed scholarship. Through this NEH grant, and the earlier Digital Start-Up Grant (HD 51330-11), WSUV and Fort Vancouver have become an epicenter of internationally significant research and innovation related to mobile media and mobile learning, generating free and accessible mobile apps, for the general public, while also advancing the digital humanities field in several demonstrable ways, which will be outlined in this report.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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