Loading… loading… The influence of download time on information search
Bibliographic record
Abstract
When browsing online, there is considerable variation in the amount of time that one has to wait for content to appear once the link to that content has been activated (i.e., clicked). In two experiments we examined how 'download time'-a potential barrier to information access-influences search behaviour. In both experiments, participants completed a video-watching task in which they were presented with a screen containing six clickable icons, each of which represented a unique video. When participants clicked an icon, a video would begin to load and then play. The participants' task was to gain as much information from the videos as possible for a later memory test. Critically, however, the 'download time' (i.e., the time between the click on the icon and the video beginning to play) of the available videos in a given search session varied. In Experiment 1, these download times were 0 (instant), 2, or 30 seconds, and in Experiment 2, they were 5, 15, or 30 seconds. In general, we found that participants terminated and avoided videos with longer download times than videos with shorter download times. Interestingly, this effect was attenuated when the experienced download times were more similar to each other (Experiment 2) than when they were more different from each other (Experiment 1).
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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 | 0.002 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".