Comparing Toronto Public Library's Kanopy Service with Traditional Classification and Subject Access Tools
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".