Bibliographic record
Abstract
The UMass Amherst Libraries hosts an exhibit, “Unseen Labor,” can be viewed through May 2022, in the Science and Engineering Library in Lederle Lowrise, Floor 2, at the University of Massachusetts Amherst. The exhibit is a library community-organizing art project created by UMass Amherst metadata librarian Ann Kardos, and consists of cross stitch and embroidery pieces that share stories about libraries, the theme of unseen labor, the work that metadata librarians do, projects they are proud of, and more. The exhibit represents approximately 60 creators from a wide variety of libraries: academic, public, museum libraries, and archives, from all over the U.S., Canada, and the United Kingdom.\nMetadata work is not typically seen as creative work, but rather work that is guided by national standards, best practices, policies, and guidelines in order to produce and maintain standard records for library resources that can be shared between institutions and vendors.\nMetadata librarians create and maintain millions of library resources for patrons, with whom they may rarely (if ever) interact, and they provide valuable backend support for their public-facing colleagues. The project asked library metadata creators to examine stories and experiences that would center their unseen labor, both physical and emotional. A companion eBook exhibition catalogue will be available.\nAnn Kardos has been a metadata librarian at the University of Massachusetts Amherst since 2017, where she works as part of a small team of dedicated individuals supporting access to approximately seven million records in the Five College Catalog. Kardos learned how to cross stitch as a child and took it up again during the pandemic for stress relief. In November 2021, she had an original embroidery piece on display at the National Liberty Museum in Philadelphia, as part of the Badass Herstory exhibit curated by artist and activist Shannon Downey, who goes by the name Badass Cross Stitch.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.017 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.133 | 0.035 |
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".