Crowdsourcing Practices in Academic Libraries in Nigeria
Why this work is in the frame
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Bibliographic record
Abstract
Objective – In this study, we investigated the utilization of crowdsourcing practices among academic librarians in Nigeria, encompassing all 36 states across the 6 geopolitical zones of the country. Methods – We employed the descriptive survey design. The target population consisted of academic librarians who were members of the national professional online group of the association known as the NLA where scholars shared professional thoughts and advancements. Results – The findings revealed a high level of awareness about crowdsourcing among academic librarians, with their experiences spanning various areas such as knowledge discovery and management (RII = 0.76), broadcast search (RII = 0.63), the distribution of human intelligence tasking (RII = 0.62), and peer-vetted creative production (RII = 0.59). In terms of the extent of practice, electronic document exchange services received the highest relative importance index score (RII = 0.73), followed closely by e-payment platforms (RII = 0.73). The findings also indicated that crowdsourcing is considered beneficial for collection development (RII = 0.68) and is perceived to be useful in the procurement of new items for the library (RII = 0.67). However, the study identified inadequate institutional support (RII = 0.91) as the foremost challenge impeding the adoption and implementation of crowdsourcing practices in academic libraries in Nigeria. Other challenges included inadequate electricity supply and unstable Internet network systems in Nigeria which has hindered full deployment of crowdsourcing in academic library settings in the country. Conclusion – This study emphasized the importance of the adoption and implementation of crowdsourcing practices in academic libraries in Nigeria. Addressing challenges related to institutional support, electricity supply, and Internet connectivity is crucial to creating an enabling environment for successful crowdsourcing initiatives.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.516 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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