Bibliographic record
Abstract
For decades academic libraries have been buying databases to support classroom instruction and faculty research.The library market for these resources is well established, with specialized salespeople who know what libraries want and understand how to deliver the information.Data resources are made widely available for use, usually to the whole campus.Librarians customarily purchase database subscriptions with the intention of renewing them annually for perpetuity.Examples of web-based business databases in the academic market include Mergent Online and Capital IQ.Some, such as CRSP or TAQ, use platforms like WRDS to make their products available to libraries.The largest information producers use a mixed approach.For example, Thomson Reuters and Standard & Poor's both sell an array of information products across platforms.Academic decision-makers have commonly considered the library's information purchases to be community resources.Librarians purchased data that were widely in demand and readily available to a wide cross-section of users and declined to purchase resources that were available to only a small group or an individual.Even when influential faculty researchers requested specific databases, in most cases librarians made purchase decisions based on overall demand for the resource.Assistance with using specialized data was limited.But now user needs for data are changing, and use of libraries' web-based and WRDS-accessible databases is declining.With increasing frequency, faculty members are requesting a different kind of resource-stand-alone data sets that are not widely available to the library market and not available through WRDS.The seller often withholds university-wide use, and in many cases is not set up to offer it.Librarians who recognize the change in faculty data needs and want to stay relevant in the university research ecosystem are now purchasing and licensing these unique data sets to make them available to individual researchers or small groups of faculty.These stand-alone data sets are usually custom extracts, often one-time purchases of specific data that an individual or small group of faculty members needs for their research.These data sets involve a number of licensing, hosting, curating, and funding issues that librarians must resolve if they want to serve their institutional research agenda.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.034 | 0.024 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".