Bibliographic record
Abstract
Two studies are reported, comparing the browser loading times of webpages created using common Web development techniques. The loading speeds were estimated in 12 Asian countries by members of the PANdora network, funded by the International Development Research Centre (IDRC) to conduct collaborative research in the development of effective distance education (DE) practices. An online survey tool with stopwatch-type counter was used. Responses were obtained from Bhutan, Cambodia, India, Indonesia, Laos, Mongolia, the Philippines, Sri Lanka, Pakistan, Singapore, Thailand, and Vietnam. In most of the survey conditions, browser loading times were noted up to four times slower than commonly prescribed as acceptable. Failure of pages to load at all was frequent. The speediest loading times were observed when the online material was hosted locally, and was created either in the Docebo learning management system (LMS), or in the HTML option provided by the Moodle LMS. It is recommended that formative evaluation of this type should become standard practice in the selection and use of online programming techniques, in order to preserve the accessibility of the World-Wide-Web across large geographical distances, as for DE in the developing world.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".