Bibliographic record
Abstract
This chapter presents recommendations for reporting context in studies of Web usage including Web browsing behavior. These recommendations consist of eight categories of contextual information crucial to the reporting of results: user characteristics, temporal information, Web browsing environment, nature of the Web browsing task, data collection methods, descriptive data reporting, statistical analysis, and results in the context of prior work. This chapter argues that the Web and its user population are constantly growing and evolving. This changing temporal context can make it difficult for researchers to evaluate previous work in the proper context, particularly when detailed information about the user population, experimental methodology, and results is not presented. The adoption of these recommendations will allow researchers in the area of Web browsing behavior to more easily replicate previous work, make comparisons between their current work and previous work, and build upon previous work to advance the field.
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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 | 0.128 | 0.456 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.057 | 0.071 |
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".