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Record W2277327449 · doi:10.11575/prism/29834

Assessing discovery; user discovery pathways

2014· article· en· W2277327449 on OpenAlexaboutno aff
Susan Beatty, Rhiannon Jones, Laura Koltutsky

Bibliographic record

VenueOpen MIND · 2014
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceData science

Abstract

fetched live from OpenAlex

Following up on the user assessment conducted by the Discovery User Experience team at the University of Calgary, a subcommittee further analyzed usability through paths taken by users to discover known and unknown items. The goal was to examine user preferences and behaviours when introduced to the "Bento Box" discovery interface. Tasks were designed to mimic natural and frequent searches by the majority of our users. Individual research pathways were identified in order to determine possible commonalities and significant differences in user search behaviours. The speakers will present the results of their analysis and discuss possible improvements to search results display from a user point of view.

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)0.083
metaresearch head score (Gemma)0.211
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 score0.083
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0830.211
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0160.008
Science and technology studies0.0040.003
Scholarly communication0.0100.011
Open science0.0030.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.330
Teacher spread0.259 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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