Bibliographic record
Abstract
An evidence based approach to library practice involves the ability to identify problem areas in the library, review and evaluate relevant evidence, design and implement rigorous research approaches, and apply strategies for improvement.Increasingly, AI is being incorporated into various facets of library and information work, including search tools and discovery layers, reference assistance, metadata generation, digital preservation, predictive analytics in collection development, information literacy instruction, accessibility services, and image recognition.In order to competently implement evidence based practice (EBP) in libraries, it is becoming imperative for library professionals to have some understanding of AI technologies and their impact on society.Evidence based practice is a way of making decisions based on the integration of research evidence, professional expertise, and user values and experiences (Sackett et al., 1996).In the field of library and information science, this means that practitioners seek out the best available evidence to answer their questions, whether that evidence comes from prior literature, original research studies, or local evidence sources such as statistics, assessments, and observations (Koufogiannakis & Brettle, 2016).To adopt an evidence based approach to practice, library professionals have to be well versed in the process and skilled in a number of different areas, such as analyzing problems, synthesizing literature, designing program evaluations and research studies, collecting and analyzing data, critically appraising research, generating solutions, and making decisions (Koufogiannakis & Brettle, 2016).
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 | 0.123 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.009 | 0.079 |
| Scholarly communication | 0.042 | 0.025 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.019 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".