Scrubbed data on Wikipedians in Residence in Libraries based on the Mapping GLAM-Wiki collaborations
Bibliographic record
Abstract
<strong>Source</strong>: GLAM-Wiki Activities Mapping - Community Review and Feedback Sheet <strong>Source's context: </strong> Gill, Satdeep. ‘Mapping GLAM-Wiki Collaborations’. <em>This Month in GLAM</em>, March 2020. https://outreach.wikimedia.org/wiki/GLAM/Newsletter/March_2020/Contents/WMF_GLAM_report. Data was scrubbed using OpenRefine 3.4.1 The original spreadsheet had only partial information in many fields and it is a work in progress (for more see the "source's context" link above). I have only manually double checked those rows in which the “Primary partner institution” contains the stem “libr*” or “bibli*. The following eight rows where modified and “Library” was added in the “Type of institution” column: Municipal Library, Patiala, BRAU Library of the University of Naples Federico II, Library and Archives Canada, Eötvös Loránd University Library and Archives, National Health Library and Knowledge Service, National Doctors Training and Planning, Daniel Cosío Villegas Library, Cantonal and University Library, Nationaal Archief | Koninklijke Bibliotheek; and “Library association” was added to the Online Computer Library Center (OCLC) entry. All changes can be seen in the WiRs-in-libraries_MGW_scrubbing-changes.json file in this release.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".