LOEX‐of‐the‐West 2012: creative landscapes in southern California
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce the special issue of Reference Services Review entitled “LOEX‐of‐the‐West 2012: creative landscapes in southern California”. Design/methodology/approach Over 160 librarians from across the USA and Canada attended the biennial LOEX‐of‐the‐West (LOTW) conference on the campus of Woodbury University in Burbank, California from June 6‐8, 2012. LOTW strives for an atmosphere in which speakers can share innovative ideas and open a dialog with other librarians. Findings Traditionally, after each LOEX‐of‐the‐West (LOTW) conference a number of papers based on session presentations are submitted to Reference Services Review (RSR) for publication. Building on their work at the 2012 preconference, Editors of RSR, Ms Eleanor Mitchell and Ms Sarah Barbara Watstein, have worked closely with presenters to transform their talks to published papers. After going through a double blind peer review process, seven papers have been selected for publication in this issue. Originality/value The authors/Guest Editors are excited to share these papers in this special LOEX‐of‐the‐West issue of Reference Services Review. It is indeed just as the conference theme stated “Information Literacy for all Terrains”.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".