Bibliographic record
Abstract
Privacy, understood as one’s calibrated accessibility to others, is a necessary condition for fostering personal and shared identities and exercising individual and collective agency. Privacy literacy entails conceptual knowledge, practical skills, and social attitudes for managing one’s personal information and understanding its role in the information ecosystem and in society. This poster highlights the emergence of library-based privacy education programming and invites feedback on practitioner-facing works-in-progress resulting from Libraries Stand for Privacy: National Forum for Privacy Literacy Standards and Competencies. The spring 2025 national forum convened more than fifty privacy literacy educators from public, school, and academic libraries in the United States and Canada, along with allied LIS scholars and independent information professionals, who engaged in hybrid, participant-led roundtable and working group sessions to ideate professional competencies and learning standards for privacy education programming in libraries. Following the forum, select working groups analyzed artifacts from these participatory research sessions, including both individual- and group-authored notes, to develop draft consensus frameworks, competencies, and practitioner resources for coordinating privacy literacy programming in libraries across the K-20 education spectrum. Attendees will learn effective strategies for implementing hybrid participatory research methods that are inclusive to library workers from all institution types, and will gain access to pilot practitioner materials to support library-based privacy literacy programming for review and feedback. Attendees will also gain appreciation for the importance of privacy to intellectual freedom, individual agency and identity, and collective action for social justice, and for how library-based privacy literacy programming can enrich a privacy-conducive culture.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".