Alignment of competencies as identified by library and information science educators and practitioners : a case study of database management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Library and Information Science (LIS) education must equip its graduates with the level of competence commensurate with the demands of entry-level positions available in the field. This is more so in the area of information technology (IT) that is widely acknowledged to be rapidly evolving thereby offering unique job specifications and or positions in LIS. This exploratory research investigates the extent of alignment between the level of competence proposed in learning objectives by LIS educators, and the level of competence required from LIS graduates by practitioners in the field. The study focuses specifically on cognitive competence, and in the domain of database management (DBM) within LIS education in US and Canada. The Taxonomy Table (TT) designed by Anderson and Krathwohl (2001) was used as a conceptual framework, to analyze learning objectives obtained from DBM educators and practitioners to determine the levels of competence proposed by educator and practitioners in DBM. The levels of competence derived from educators and practitioners were then compared to determine the extent of alignment between the levels of competence offered by LIS educators, and the levels of competence expectations of LIS practitioners from graduates in DBM.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it