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Record W4403433531 · doi:10.1002/pra2.1176

Enabling Serendipity During Digital Library Search

2024· article· en· W4403433531 on OpenAlex
Ei Ei Mon, Orland Hoeber

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2024
Typearticle
Languageen
FieldComputer Science
TopicInformation Retrieval and Search Behavior
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSerendipityDigital libraryComputer scienceWorld Wide WebArtLiteraturePhilosophyEpistemology

Abstract

fetched live from OpenAlex

ABSTRACT When a serendipitous discovery is made while searching within a digital library collection (such as an academic digital library at a university), the searcher has a difficult choice to make: either pursue the serendipitous discovery or set it aside and deal with it later. If they take the first option, this breaks the flow of the primary search activity which may make it difficult to resume. If they take the second option, they may have difficulty re‐finding what they discovered when they are finished with the primary search activity. We have developed a novel search interface that includes topic‐based workspaces and a “read it later” list. Serendipitous discoveries can be easily added to the “read it later” list, allowing the searcher to stay focused on their current search activity knowing that they can easily return to the discovered resource. For each resource saved to the “read it later” list, a textual similarity is calculated against the collection of documents saved in each of the searcher's workspaces. This allows them to easily identify which of their prior search tasks is a best fit for the discovery, as well as an ability to create a new workspace if desired.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.018
Open science0.0010.001
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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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