Call for Classic Research Studies: Evidence Based Library and Information Practice
Bibliographic record
Abstract
Can you identify a research study that has had a major influence on your practice or library and information practice in general? Are you keen to make more people aware of this research and its value? Are you willing to write a summary and appraisal of this research? Evidence Based Library and Information Practice (EBLIP) http://ejournals.library.ualberta.ca/index.php/EBLIP is seeking nominations of classic research studies that have impacted practice, had an influence on LIS researchers, and stood the test of time. We need your help to identify these classic studies in our field and commit to writing a summary of that research. Volume 2, Issue 4 (December 2007) of EBLIP published six such Classics, featuring research from Carol Kuhlthau, Joanne Gard Marshall, Robert S. Taylor, and more. We want to continue to highlight past research that is important and bring that research to the attention of new readers. Please consider nominating a great research article to be featured in EBLIP. For more information, or to nominate a research article, please contact Denise Koufogiannakis denise.koufogiannakis@ualberta.ca. Nominations should be accompanied by a full bibliographic citation and an explanation of the contribution of the research to the field of library and information practice. If the article is selected, a schedule for publication and deadlines will be arranged with the Editor. Thanks, Denise Associate Editor (Classics) Evidence Based Library and Information Practice
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 | 0.216 | 0.448 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.040 | 0.045 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.031 | 0.032 |
| Insufficient payload (model declined to judge) | 0.058 | 0.040 |
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".