Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyze a number of issues related to both education for and the practice of reading and readers’ advisory in library and information science (LIS). Written from the standpoint of an LIS educator, the paper is addressed to LIS professors, future and current LIS students, and public services librarians working in all types of libraries, including academic and special, because the practice of reading is no longer limited to school and public libraries. Librarians’ expertise can also benefit a larger community outside of the library walls, which would take outreach and embeddedness to an entirely new level. Design/methodology/approach – The paper analyzes the situation in LIS education and reading practices based on a vast array of published sources and the author’s personal experience as an LIS educator. Findings – The following problematic points are raised: modeling reading work and education for reading after information services and information science education, respectively; outdated pedagogical approaches; insufficient user orientation and excessive focus on materials; limiting reading activities to one to two types of libraries; insufficient community outreach; and, in general, the prevalence of responsive rather than proactive practices. Originality/value – The paper proposes some solutions for the identified problems, the implementation of which depends on the collective effort and the collective will. However, it does not offer a particularly optimistic or upbeat view on the possibility of swift and sweeping changes.
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 | 0.021 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".