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Record W2136243608 · doi:10.1518/hfes.46.3.385.50404

Age Differences in Search of Web Pages: The Effects of Link Size, Link Number, and Clutter

2004· article· en· W2136243608 on OpenAlexafffund
Michael Grahame, Jason Laberge, Charles T. Scialfa

Bibliographic record

VenueHuman Factors The Journal of the Human Factors and Ergonomics Society · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsClutterWeb pageLink (geometry)Computer sciencePoint (geometry)Display sizeUsabilityWorld Wide WebInformation retrievalMathematicsHuman–computer interactionComputer networkRadarTelecommunicationsDisplay device

Abstract

fetched live from OpenAlex

Reaction time, eye movements, and errors were measured during visual search of Web pages to determine age-related differences in performance as a function of link size, link number, link location, and clutter. Participants (15 young adults, M = 23 years; 14 older adults, M = 57 years) searched Web pages for target links that varied from trial to trial. During one half of the trials, links were enlarged from 10-point to 12-point font. Target location was distributed among the left, center, and bottom portions of the screen. Clutter was manipulated according to the percentage of used space, including graphics and text, and the number of potentially distracting nontarget links was varied. Increased link size improved performance, whereas increased clutter and links hampered search, especially for older adults. Results also showed that links located in the left region of the page were found most easily. Actual or potential applications of this research include Web site design to increase usability, particularly for older adults.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.273
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations88
Published2004
Admission routes2
Has abstractyes

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