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Record W2082584912 · doi:10.1108/00220411211209195

Social discovery tools: extending the principle of user convenience

2012· article· en· W2082584912 on OpenAlexaff
Louise F. Spiteri

Bibliographic record

VenueJournal of Documentation · 2012
Typearticle
Languageen
FieldComputer Science
TopicWeb and Library Services
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWorld Wide WebMetadataWarrantComputer scienceBridge (graph theory)Internet privacyBusinessMedicine

Abstract

fetched live from OpenAlex

Purpose New social discovery systems have social‐type Web 2.0 features that allow users to enhance the content of bibliographic records by adding their own tags, ratings, and reviews. One of the primary underlying principles of cataloguing is that catalogue records be designed with the user in mind, i.e. user convenience. The purpose of this paper is to explore the relationship between the principle of user convenience and social discovery systems. Design/methodology/approach A review of the literature and codes of ethics of associations of information professions was undertaken to examine: the ethical dimensions of creating catalogue records to reflect user convenience, the relationship between culture and user convenience, and how social discovery tools can facilitate the creation of interactive and flexible catalogue records that reflect the culture(s) and needs of the library communities in which they exist. Findings Social discovery systems can address the primary barriers to creating catalogue records that meet user convenience: determining and reflecting the needs and cultural warrant of the users, and maintaining the quality and integrity of the catalogue records. Practical implications Social discovery systems can serve as a bridge between cataloguers' desire to create accurate catalogue records that conform to accepted cataloguing standards, and their ethical imperative to ensure that these records meet the needs of the clients. Originality/value The findings of this study pave the way for further research into how user‐contributed metadata allow clients to express their needs and cultural warrant and to interact with one another and library staff.

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)0.059
metaresearch head score (Gemma)0.122
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.310

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0590.122
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0080.032
Scholarly communication0.0230.036
Open science0.0030.020
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0060.002

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 head0.029
GPT teacher head0.317
Teacher spread0.288 · 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.

Study designTheoretical or conceptual
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

Citations9
Published2012
Admission routes1
Has abstractyes

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