Bibliographic record
Abstract
Evidence Based Library and Information Practice (EBLIP) is soliciting nominations for a special feature in our December 2007 issue. We will be featuring summaries 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. The summaries will use a format similar to that of the current Evidence Summaries published in EBLIP, but with a commentary that focuses on the impact of the research since it was published. Please give this some thought and consider nominating a great research article to be featured in EBLIP! Dates to note: Nomination deadline -- May 30, 2007 Notification of acceptance for the feature issue -- June 30, 2007 Submission deadline for the summary -- September 1, 2007 Publication date -- December 15, 2007 For more information, or to nominate a research article, please contact Denise Koufogiannakis: e-mail: 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.
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 | 0.167 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.098 | 0.071 |
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".