Bibliographic record
Abstract
Libraries invest significant resources in collecting data and reflecting on the tremendous value they bring to their communities. Unfortunately, the message sometimes fails to have a significant impact beyond the safe confines of the assessment committee room. This paper focuses, from a practical perspective, on strategies for communicating the value message to internal and external audiences (including senior library managers and staff, donors, campus administrators, etc). Some basic concepts, such as simplicity and conciseness, apply to all audiences while other factors must be tailored to the group being targeted. For example, senior leaders will be more persuaded by clear linkages to the strategic plan while campus administrators may be more compelled by rankings and reputation. The author suggests that, to be truly effective, the library value conversation must be carefully planned in a systematic, creative and highly customized way. Libraries must identify the key stakeholders for a particular message, then map out the specific content to be shared and the specific approach or medium to be used for conveying that content to that audience.
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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.027 | 0.024 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".