BiblioComprehension
Bibliographic record
Abstract
Increasingly, public libraries are incorporating interactive, collaborative, and user-centred social discovery tools into traditional library services with the goal of better serving their patrons. These tools are designed to encourage communication and interaction between library patrons and staff by providing a platform for patrons to evaluate, comment on, create, and share personalized lists of their favourite items in a library’s collection. BiblioCommons is one example of a discovery tool that has been embraced by public libraries and their patrons to this end. Yet, while tools such as BiblioCommons offer many benefits to library patrons, relying on these tools to deliver core library services may violate patron privacy and confidentiality. Using the American Library Association Code of Ethics and the Library Bill of Rights as a framework, we explore the websites of Canadian public libraries that use BiblioCommons to discover how these libraries communicate privacy concerns associated with the use of this service to their patrons. Based on our findings, we argue that libraries are largely failing in their ethical responsibility to alert patrons to the privacy and confidentiality concerns associated with BiblioCommons.
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 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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.020 | 0.011 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".