Bibliographic record
Abstract
This article discusses “Crossing Over,” a pedagogical art / performance project linking university students around the world that investigates the notions of cosmopolitanism and mobility as ways to constitute meaningful social networks by exchanging virtual performances—and suitcases—over the internet. The questions that the project asks are critical in light of the globalization of information that the World Wide Web and other crossing over points represent. While globalization opens borders to all manner of material exchanges (including people), endless digital data stream through the Internet portal providing opportunities to trade on personal information. We explore and share our identity at our peril. “Crossing Over” also explores the idea that there is an intrinsic relationship between embodied presence and one’s place in the world. Performing or representing who we are is indistinguishable from the place from which we come. The Internet shows us that the experience of presence is manifold and strongly manifest in virtual environments. Cyberspace is not a non-place—it is the ever-mutable backdrop, the mirror held up to a virtual spectator—who will always see something more than a mere reflection—will see differently based on his/ her place in the world.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".