Matching Made in Heaven: Collections and Metadata Collaboration for Print Preservation
Bibliographic record
Abstract
Following the trend of repurposing library space to meet modern user needs, Western University is undergoing a planned revitalization and renovation of its largest library on campus. As a result, 500,000 items will need to be shifted to other locations or off-site storage. In this session we will outline the impact of metadata work in shifting this large collection of material to a shared print preservation storage facility, in coordination with Western University’s Keep@Downsview partnership (https://downsviewkeep.org/). Keep@Downsview is a partnership of five universities to preserve the scholarly record in Ontario in a shared, high-density storage and preservation facility. We will demonstrate the importance of collaboration and communication between Collections Librarians and Metadata Librarians to improve identification of materials for shared print preservation. While past Charleston conference presentations have discussed weeding legacy print collections, this session will focus on the importance of metadata matching processes. Speaking from experience at Western University, we will identify the types of tools and skills that we use to facilitate this work (such as MarcEdit, Excel, Python, OpenRefine, Google Sheets, and regular expressions). In highlighting the value of metadata for collections based projects, attendees will walk away with talking points to advocate for quality metadata at their institution and with vendors.
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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.020 | 0.007 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".