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Record W2151875870 · doi:10.1002/asi.10174

Investigating how individuals conceptually and physically structure file folders for electronic bookmarks: The example of the financial services industry

2002· article· en· W2151875870 on OpenAlexaff
Lisa Gottlieb, Juris Dilevko

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2002
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceCategorizationSample (material)Set (abstract data type)Domain (mathematical analysis)Variation (astronomy)Categorical variableFlexibility (engineering)Process (computing)StandardizationFinancial servicesOrganizational structureKnowledge managementWorld Wide WebFinanceBusinessManagement

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.301
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2002
Admission routes1
Has abstractyes

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