Crowdsourcing Practices in Academic Libraries in Nigeria
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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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".