Bibliographic record
Abstract
Have you ever wondered why many devices that we use daily are so difficult to use? Why has no one designed a universal adapter for all the tools and equipment that use rechargeable batteries? If you’re anything like me, you could likely fill drawers with all the different adapters you have acquired for cellphones, computers and peripherals, BluetoothTM accessories, portable music players, cordless phones, drills and the like; not to mention all the remote controls for televisions, sound systems, and VCR, CD, DVD and Blu-ray DiscTM players. Consider for a moment: Have you ever owned an appliance or device that you particularly liked? Why did you like it? Was it was easy to use? Did it have intuitive displays, or labels that were easy to read and understand? Or did you simply like the appearance of the device – sleek, colourful, modernistic? Companies like Apple have led their competitors in intuitive design features with products such as Mac computers and the iPod. For many years, Apple has offered product lines that are interoperable, aesthetically appealing and intuitive to use. They have also recognized the importance of interoperability with competitors’ products.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.011 |
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".