Bibliographic record
Abstract
Managing electronic resources is a fairly complex process faced by librarians with ever more frequency in today’s digital environment. In an effort to approach the possibility of purchasing an electronic resource manager (ERM), electronic resource workflow processes were investigated and documented. The life cycle of electronic resources takes a very different form than that of its print counterpart, and it can prove immensely useful to the library to examine these workflows. Such workflow documentation can offer the opportunity for analysis, exposure of problem areas, occurrences of overlap or duplication, and can lead to discussions amongst faculty and staff that are crucial to the smooth running of the institution. This talk will examine the methodology and framework used to document these workflows. It involves interviews with staff and faculty involved in these procedures, discussions with stakeholders at different levels of the electronic workflow, and clarification of the steps involved in these electronic workflows. Once the workflows have been documented, they will undergo analysis. This strategy can expose “gaps” in the procedure, indicate where the workflow can be streamlined, and encourage conversations within the library departments that can lead to new and more effective workflows.
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 | 0.038 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".