Investigating how individuals conceptually and physically structure file folders for electronic bookmarks: The example of the financial services industry
Bibliographic record
Abstract
Abstract Personal preferences in the development of categorical folders for bookmarks are examined in terms of both the choice and definition of folder domain and the overall structure of the folder system. Study participants from the financial industry were asked to organize the same set of finance‐related bookmarks from a given list, as opposed to describing their organizational approaches using their own personal bookmarks, so that the organizational systems could be compared across the sample. The selection of folder domain is influenced by contextual factors such as intended use and relevancy to current projects. Similarly, the structure of the overall folder system was determined in part by participants' navigational preferences. While the majority of participants created folders that cover the topics of finance, government, accounting, news, law, and tax, the actual definition of these folders and the criteria for inclusion vary across the sample. Furthermore, these criteria cannot be readily discerned from the folder system itself. Variation in folder domain and definition could adversely affect the utility of bookmark management systems for multiple users that involve some degree of standardization. The same variation in interpretation of seemingly identical folders suggests that systems with automatic categorization would not provide users with enough flexibility in how they could organize and access their bookmarks.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".