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Record W1585681624 · doi:10.1002/asi.22610

An examination of multisession web tasks

2012· article· en· W1585681624 on OpenAlexaff
Bonnie MacKay, Carolyn Watters

Bibliographic record

VenueJournal of the American Society for Information Science and Technology · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicPersonal Information Management and User Behavior
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer scienceSession (web analytics)World Wide WebTask (project management)Variety (cybernetics)Web applicationHuman multitaskingThe InternetMultimediaWeb navigationHuman–computer interactionInformation retrievalArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

Today, people perform many types of tasks on the web, including those that require multiple web sessions. In this article, we build on research about web tasks and present an in‐depth evaluation of the types of tasks people perform on the web over multiple web sessions. Multisession web tasks are goal‐based tasks that often contain subtasks requiring more than one web session to complete. We will detail the results of two longitudinal studies that we conducted to explore this topic. The first study was a weeklong web‐diary study where participants self‐reported information on their own multisession tasks. The second study was a monthlong field study where participants used a customized version of F irefox, which logged their interactions for both their own multisession tasks and their other web activity. The results from both studies found that people perform eight different types of multisession tasks, that these tasks often consist of several subtasks, that these lasted different lengths of time, and that users have unique strategies to help continue the tasks which involved a variety of web and browser tools such as search engines and bookmarks and external applications such as N otepad or W ord. Using the results from these studies, we have suggested three guidelines for developers to consider when designing browser‐tool features to help people perform these types of tasks: (a) to maintain a list of current multisession tasks, (b) to support multitasking, and (c) to manage task‐related information between sessions.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.008
Open science0.0010.000
Research integrity0.0000.000
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 head0.101
GPT teacher head0.421
Teacher spread0.320 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other 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

Citations12
Published2012
Admission routes1
Has abstractyes

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