Bibliographic record
Abstract
Objective – This narrative literature review examines how values and a values-based practice framework are positioned as significant to evidence based practice in libraries. This includes examining the partnership between values and evidence in decision making and reflective practice. The review responds to a gap in the literature on the origins and application of values-based practice in evidence based library and information practice (EBLIP). Methods – Searches for this narrative review were conducted in library and information science databases, discovery tools, and individual journals. Forward and backward citation searches were also undertaken. Searches aimed to encompass both the EBLIP and library assessment literature. Research and professional publications were considered for inclusion based on their engagement with values and values-based practice in EBLIP processes and decisions. Results – The findings highlight how values reflect positionality, driving action and decision making in all stages of evidence based practice in libraries. The literature emphasizes the role of values when practitioners engage with critical reflective practice or invite user voices in evidence. An explicit values-based practice approach was evident in the library assessment literature, though not explicitly addressed in the EBLIP literature or EBLIP models. This is despite a partnership between evidence based practice and values-based practice in the health sciences literature, with literature on person-centred approaches aiming to relate evidence to individuals. Conclusions – The EBLIP literature could further examine how values reflect positionality and drive action and decision making across all stages of evidence based practice. Values-based practice offers an opportunity to critically reflect on whose voices, perspectives, and values are reflected in and contribute to the library and information science evidence base.
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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".