Educating Assessors: Preparing Librarians with Micro and Macro Skills
Bibliographic record
Abstract
Abstract Objective – To examine the fit between libraries’ needs for evaluation skills, and library education and professional development opportunities. Many library position descriptions and many areas of library science education focus on professional skills and activities, such as delivering information literacy, designing programs, and managing resources. Only some positions, some parts of positions, and some areas of education specifically address assessment/evaluation skills. The growth of the Library Assessment Conference, the establishment of the ARL-ASSESS listserv, and other evidence indicates that assessment skills are increasingly important. Method – Four bodies of evidence were examined for the prevalence of assessment needs and assessment education: the American Library Association core competencies; job ads from large public and academic libraries; professional development courses and sessions offered by American Library Association (ALA) divisions and state library associations; and course requirements contained in ALA-accredited Masters of Library Science (MLS) programs. Results – While one-third of job postings made some mention of evaluation responsibilities, less than 10% of conference or continuing education offerings addressed assessment skills. In addition, management as a topic is a widespread requirement in MLS programs (78%), while research (58%) and assessment (15%) far less common. Conclusions – Overall, there seems to be more need for assessment/evaluation skills than there are structured offerings to educate people in developing those skills. In addition, roles are changing: some of the most professional-level activities of graduate-degreed librarians involve planning, education, and assessment. MLS students need to understand that these macro skills are essential to leadership, and current librarians need opportunities to add to their skill sets.
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 | 0.026 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".