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Record W7018014174

Comparing Toronto Public Library's Kanopy Service with Traditional Classification and Subject Access Tools

2023· article· en· W7018014174 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueScholarship@Western (Western University) · 2023
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsnot available
Fundersnot available
KeywordsBridging (networking)Digital libraryService (business)Interface (matter)Subject (documents)User interfaceService designDigital transformation
DOInot available

Abstract

fetched live from OpenAlex

The ongoing transformation of libraries in the digital age is marked by the adoption of new technologies and innovative services to cater to the evolving needs of users. One such example is the integration of streaming platforms like Kanopy into library systems, offering a convenient and user-friendly experience to patrons. The Toronto Public Library (TPL) has adopted Kanopy to provide its users with access to a diverse range of films and documentaries, effectively bridging the gap between traditional library classification and subject access tools, and modern streaming services (Digital Library Services, n.d.). This study aims to analyze the effectiveness of TPL's Kanopy service in addressing user needs and expectations and to explore how the service compares to other popular streaming platforms. The paper will examine the user experience of Kanopy, including its interface design, search features, and user satisfaction. Furthermore, the study will investigate the challenges faced in incorporating non-library features into library services and the implications for library interface design (Mundt & Medaille, 2011). By conducting this analysis, the paper seeks to contribute to a better understanding of how libraries can adapt and innovate to meet the changing demands of users in the digital age.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0050.037
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 head0.278
GPT teacher head0.297
Teacher spread0.018 · 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