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Record W2067520386 · doi:10.1089/10949310151088352

How Spatial Is Hyperspace? Interacting with Hypertext Documents: Cognitive Processes and Concepts

2001· article· en· W2067520386 on OpenAlexaff
Patricia Boechler

Bibliographic record

VenueCyberPsychology & Behavior · 2001
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsConceptualizationCognitionCyberspaceHypertextComputer scienceComponent (thermodynamics)HyperspaceCognitive scienceHuman–computer interactionPerceptionMental representationIdentification (biology)Data scienceWorld Wide WebPsychologyThe InternetArtificial intelligence

Abstract

fetched live from OpenAlex

The World Wide Web provides us with a widely accessible technology, fast access to massive amounts of information and services, and the opportunity for personal interaction with numerous individuals simultaneously. Underlying and influencing all of these activities is our basic conceptualization of this new environment; an environment we can view as having a cognitive component (hyperspace) and a social component (cyberspace). This review argues that cognitive psychologists have a key role to play in the identification and analysis of how the processes of the mind interact with the Web. The body of literature on cognitive processes provides us with knowledge about spatial perceptions, strategies for navigation in space, memory functions and limitations, and the formation of mental representations of environments. Researchers of human cognition can offer established methodologies and conceptual frameworks toward investigation of the cognitions involved in the use of electronic environments like the Web.

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)0.002
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.015
Scholarly communication0.0110.014
Open science0.0010.002
Research integrity0.0020.001
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 head0.016
GPT teacher head0.293
Teacher spread0.277 · 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

Citations62
Published2001
Admission routes1
Has abstractyes

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