Data Expeditions: Mining Data for Effective Decision-Making
Bibliographic record
Abstract
Beyond library budgets and content usage reports, libraries and consortia are searching, sorting, managing, and hunting for deep data that allows them to understand their environments and represent themselves and their patrons more effectively in these changing and complicated times. But data challenges exist at every turn. Finding data, which is often housed in a variety of disparate sources, is the first challenge but it is immediately followed by measuring, adapting, and distilling data down to the most important factors. Libraries and consortia spend many person hours gathering data from scratch and then deriving information and knowledge from that data to make informed, evidence-based decisions.In this session, we will hear from leading library experts about their scholarly publishing data hunting expeditions and the innovative ways they access and utilize deep data to inform their discussions and decisions and support their activities.
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 | 0.045 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".