Selecting versus Describing: A Preliminary Analysis of the Efficacy of Categories in Exploring the Web
Bibliographic record
Abstract
Summary of Results The 48 participants spent about 7 minutes doing each task. They used the search box forabout 66% of the tasks and selected from the directory categories for the remainder. On average,they examined about 5 URLs and about 6 links within each of those URLs. They tended to selectabout the fourth item on a hitlist and on average examined about two pages of hitlists. Participants reported little familiarity with the topics for each of the assigned tasks, with fewhaving ever done a search on any of the topics prior to the session. On a five-point scale with onebeing the poorest rating and five being the best rating, they indicated the degree of certainty withwhich they found their answer, the ease of finding the answer, and their satisfaction with theprocess of finding their answer at around four. User-Specified vs. Researcher Specified Task Half the questions were completely specified and half were fill-in-the-blanks, allowing someuser modification toward personalizing the task. There were no significant differences between thetwo types on any measure. This finding challenges the assumption that information retrievalexperimentation with pre-defined queries alters user behaviour in experimental settings. Ourparticipants performed about the same regardless of whether they were assigned a task orallowed to create their own. That said, it is likely that the artificially of the process, e.g., timeconstraints, lab setting, and so on, may have a greater impact than the nature of the task.
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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 | 0.020 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".