Are Best Practices Really Best? A Review of the Best Practices Literature in Library and Information Studies
Bibliographic record
Abstract
Objective - The term “best practice” appears often in library and information science literature, yet, despite the frequency with which the term is used, there is little discussion about what is meant by the term and how one can reliably identify a best practice. Methods – This paper reviews 113 articles that identify and discuss best practices, in order to determine how “best practices” are distinguished from other practices, and whether these determinations are made on the basis of consistent and reliable evidence. The review also takes into account definitions of the term to discover if a common definition is used amongst authors. Results – The “evidence” upon which papers on “best practices” are based falls into one of the following six categories: 1) opinion (n=18, 15%), 2) literature reviews (n=13, 12%), 3) practices in the library in which the author works (n=19, 17%), 4) formal and informal qualitative and quantitative approaches (n=16, 14%), 5) a combination of the aforementioned (i.e., combined approaches) (n=34, 30%), and 6) “other” sources or approaches which are largely one of a kind (n=13, 12%). There is no widely shared or common definition of “best practices” amongst the authors of these papers, and most papers (n=94, 83%) fail to define the term at all. The number of papers was, for the most part, split evenly amongst the six categories indicating that writers on the subject are basing “best practices” assertions on a wide variety of sources and evidence. Conclusions – Library and information science literature on “best practices” is rarely based on rigorous empirical methods of research and therefore is generally unreliable. There is, in addition, no widely held understanding of what is meant by the use of the term.
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 | 0.146 | 0.310 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.031 | 0.048 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".