Bibliographic record
Abstract
The recent COVID pandemic has demonstrated that distance learning is no longer a function of broadcasting conventional classroom content to a decentralized audience. Rather than perpetuate disengaged dissemination of content commonplace in in-person teaching environments, educators have aspired to elicit engagement with diverse and rich content available on the internet. This is merely a harbinger of increased demand by students and educators alike for more robust and interactive content. To meet this ambition, an initiative to create a virtual simulation for architecture students to immerse themselves in a historic Canadian First Nations settlement from centuries in the past to better understand the parallels between indigenous approaches to architecture and contemporary praxis. Drawing upon video game infrastructure, the downloadable content fostered accurate and detailed depictions of various building systems as reconstructed as a collaboration between architecture, archaeology, and game design faculty. Rather than simply presented with authoritative facts, within this highly detailed open world, students were able to engage and explore content on their own in understanding the commonalities with contemporary design strategies that provided a greater experiential learning capacity.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.013 |
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".