Bibliographic record
Abstract
Purpose This bibliography aims to give citations and annotations for a core selection of sources on the information and learning commons trend in academic libraries. Design/methodology/approach Articles, books, and web sites relevant to this topic were found in the Library, Information Science & Technology Abstracts database; Library Literature Index; WorldCat; and on the internet. Sources were chosen that contribute to an overview of the concepts or cover practical considerations in implementation. Findings Libraries are developing best practices as they experiment with learner‐centered service models, but they apply these best practices differently according to their unique needs. Early implementations focus on technology and access, while later implementations focus on more collaborations surrounding learner‐centered pedagogies. Research limitations/implications This bibliography selects from English language books, web sites, and peer reviewed journals about US, British, Canadian, and Oceania academic libraries, large and small. Originality/value This survey of the literature will help librarians and administrators understand the theoretical trends and collaboration that influence how libraries can change service, space, and technology to meet emerging needs.
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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.035 | 0.051 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.126 | 0.095 |
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".