Bibliographic record
Abstract
I begin this article with the fundamental premise that wearable computing will fundamentally improve the quality of our lives [1]. I can make this claim because for the past 20 years I have been walking around with digital eye glasses (DEG), and I believe my life has been enhanced as a result. Perhaps I am biased about wearable computing, but like my EyeTap invention that computationally processes everything I see, I try to tell it like it is. I am of course, only a one person case study, but I know there are others out there who feel the same way as I do, and perhaps for very different reasons. It is well known that when traditional optical eyeglasses were first invented, many wearers of these eyeglasses were treated poorly and discriminated against. But as time went on, society began to accept eyeglasses, even to the point where they have, in some instances, become fashion statements. Many people, who have no need for spectacles, will purchase zero prescription eyeglasses just to look smart. This says a lot about technological innovation and how society responds to it over generations of varying levels of acceptance.
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 | 0.001 | 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 | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.040 | 0.017 |
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".