Understanding the influence of librarians cognitive frames on institutional repository innovation and implementation
Bibliographic record
Abstract
Abstract An institutional repository (IR) is an information system (IS) that has the potential to promote open access to scientific knowledge and the visibility and reputation of scholars. The small number of universities in developing countries that have successfully innovated and implemented an IR hampers the actualization of these benefits. This situation calls for effort toward understanding the factors responsible. We used the snowball sampling technique to select 45 academic librarians from three university libraries in Nigeria that were involved in IR innovation. Data were collected through in‐depth interviews and participatory observation for 15 months. Thematic data analyses show that the relationships between academic librarians' IR innovation cognitive frames (belief, scripts, and resistance routines) influence IR innovation outcomes. The result is a research model that explicates how the relationships between academic librarians' cognitive frames and internal functioning of libraries constitute IR innovation barriers to IR innovation and implementation.
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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 | 0.020 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".