Alignment of competencies as identified by library and information science educators and practitioners : a case study of database management
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.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".