Librarians’ Perceptions of Tribalism/Ethnicity and Favouritism Practices and the Perceptions’ Effect on Job Satisfaction and Service Delivery in Federal University Libraries in Southern Nigeria
Bibliographic record
Abstract
Objective – This study examined librarians’ perceptions of tribalism/ethnicity and favouritism practices and the perceived effect on job satisfaction and service delivery in federal university libraries in Southern Nigeria. The study was aimed at observing whether or not librarians perceive that their senior library managers engage in tribalism/ethnicity and favouritism practices; discerning the extent to which they are perceived to be engaging in tribalism/ethnicity and favouritism practices; ascertaining how the perceived tribalism/ethnicity and favouritism practices can affect job satisfaction of librarians and service delivery by librarians in federal university libraries in Southern Nigeria. Methods – This study used a descriptive survey research design. The total population for this study comprised 296 librarians who were below the rank of senior library manager from 18 federal university libraries in the southern region of Nigeria. A structured questionnaire was used for data collection. The data collected for this study were analyzed using simple percentage/frequency counts and weighted mean. Results – This study found that librarians in university libraries in Southern Nigeria believe that senior library managers engage in tribalism/ethnicity and favouritism practices and that the extent of these practices is very high. These librarians clearly have strong concerns that the practise of tribalism/ethnicity and favouritism in libraries can have negative effects on librarians’ job satisfaction and can lead to poor service delivery. Conclusion – It is glaring from this study that tribalism and favouritism practices by senior library managers are perceived to be prevalent in university libraries in Southern Nigeria, significantly impacting librarians' job satisfaction. This negative workplace dynamic can hinder effective service delivery, ultimately affecting user experience and the library's role in education. Addressing these biases is crucial, as promoting fairness and inclusivity can enhance job satisfaction, improve staff morale, and lead to higher quality library services for all patrons.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".