User preferences in the classification of electronic bookmarks: Implications for a shared system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using the financial industry as a context, the following study seeks to address the issue of the classification of electronic bookmarks in a multi-user system by investigating what factors influence how individuals develop categories for bookmarks and how they choose to classify bookmarks within those organizational categories. An experiment was conducted in which a sample of 15 participants was asked to bookmark and to categorize 60 web sites within Internet Browser folders of their own creation. Based on the data collected during this first component of the study, individual, customized questionnaires were composed for each participant. Whereas some of the questions within these surveys focused on particular classificatory decisions regarding specific bookmarks, others looked at how the participant defined, utilized, and structured the category folders that comprised his or her classification system. The results presented in this paper focus on issues investigated in Kwasnik's (Journal of Documentation, 1991, 47, 389–398) study of the factors that inform how individuals organize their personal, paper-based documents in office environments. Whereas classificatory attributes culled from questionnaire responses nominally resembled those identified by Kwasnik, it was found that a number of these factors assumed distinctive definitions in the electronic environment. The present study suggests that the application of individual instances of classificatory attributes and the distinction between Content and Context Attributes emphasized by Kwasnik play a minimal role in the development of a multi-user classification system for bookmarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 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 it