NCompass Live: #1lib1ref: A Citation As A Gateway Into Librarianship On Wikipedia
Bibliographic record
Abstract
Wikipedia is a first stop for researchers: let's make it better! The Wikipedia Library Team at the Wikimedia Foundation are embarking on a second year of the #1lib1ref campaign, which will run January 15 through February 3, 2017 and coincides with Wikipedia's birthday. During #1lib1ref (One Librarian, One Reference) librarians each add one reference to Wikipedia. These citations to reliable sources will benefit Wikipedia readers worldwide. Alex Stinson, GLAM-Wiki (Galleries, Libraries, Archives, Museums) Strategist at the Wikimedia Foundation, and Wiki-librarians Phoebe Ayers (Massachusetts Institute of Technology), Kelly Doyle (Wikipedian in Residence for Gender Equity, West Virginia University Libraries), Merrilee Proffitt (OCLC Research), and Jessamyn West (Vermont librarian and technologist) will discuss what it means as libraries to be involved in Wikipedia and show how you can contribute to #1lib1ref.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.372 | 0.026 |
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; both teacher heads agree on what is shown here.
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".