Bibliographic record
Abstract
Many experts predicted that this, the first decade of the 21st century, will be the decade of mobile computing; although in recent years mobile technology has been one of the major growth areas in computing, the hype has thus far exceeded the reality (Urbaczewski, Valacich, & Jessup, 2003). Why is this? A recent international study of users of handheld devices suggests that there is a predominant perception that quality of service is low and that mobile applications are difficult to use; additionally, although users recognise the potential of emerging mobile technology, the study highlighted a general feeling that the technology is currently dominating rather than supporting users (Jarvenpaa, Lang, Takeda, & Tuunainen, 2003). Users are generally forgiving of physical limitations of mobile devices imposed by technological constraints; they are not, however, so forgiving of the interface to these devices (Sarker & Wells, 2003). Users can excuse restrictions on their use of mobile technology on the basis of level of technological advancement, but find it hard to accept impractical, illogical, or inconvenient interaction design.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".