Aligning Theories with Conflict Management in Higher Institutions of Learning: Averting Looming Crises to Ensure Success and Stability
Bibliographic record
Abstract
Higher institutions of learning (HIL) occasionally face conflict situations. These range from minor confrontations and demonstrations to violent strikes. The aim of this study was to align theories with conflict management in HIL to avert looming crises that might affect the core businesses of HIL. Given that conflicts are miscellaneous and disputable, managing them requires integration of various approaches and theories. Therefore, the researchers employed dual concern theory, complexity theory, and contingency theory. The empirical part of the study used the mixed approach with open-ended and closed-ended questionnaires. Data were collected from stakeholders including students, academic staff, non-academic staff, and management members of a selected HIL. The data analysis techniques used computation of means, standard deviations, frequencies, skewness, and correlations to examine the relationship between dependent and independent variables in the study. The findings of the study revealed that when students act as a group during conflicts, it more often than not becomes boundless, unpredictable, and destructive. Therefore, managers should learn when to react, how to react, and in what ways to react to find an amicable solution using conflict management theories. Because this study used the pragmatic approach to align theories with conflict management with the aim of averting looming crises in HIL, other researchers can use purely qualitative methods to validate their findings.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".