Impact of categorization autonomy on effective use and adoption intentions
Bibliographic record
Abstract
Category tree view is an omnipresent element in graphical user interfaces where it captures information in terms of a hierarchical structure. These categorization trees facilitate human users' cognitive economy and decision-making. While previous research has investigated the utilities of using unstructured data compared to pre-categorized information by business users, the effectiveness of allowing users the autonomy to create their own categorization hierarchies from generic object types remains unexplored. This paper evaluates the benefits of categorization autonomy in terms of search precision, as an objective measure, as well as subjective intentions to use the system. We examined users' interactions with a platform in information seeking tasks with 201 subjects. Our findings indicate that categorization autonomy leads to superior results, both in terms of effective use and behavioral perceptions. We also found that the impact of categorization autonomy is moderated by task flexibility, such that the benefits are more apparent in tasks that necessitate open-ended search approaches. By focusing on how user-driven categorization influences system interaction, our study contributes to the design of decision support systems that are better aligned with users' cognitive structures and task demands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".