Managing Conflict at Institution/s of Higher Learning: A Post-Positivist Perspective
Bibliographic record
Abstract
Institutions of Higher Learning in South Africa annually face challenges that often lead to student protests and demonstrations, mostly at the beginning of every academic year, which adversely impact the smooth running of academic programs. Stakeholders’ expectations were at the apex of causes that destabilise the academic environment, academic almanac and the overall academic professional reputation. The volatility of this kind retards productivity and negatively affects many tertiary institutions across the Country. This empirically grounded paper focuses on conflicting variables amongst universities, but with reference to an Eastern Cape University in South Africa spread across its Campuses. Adopting the post-positivist approach, this study obtained data from over 180 respondents and the data was analysed by using descriptive and inferential statistics, including analyses of variance and Pearson Product Moment correlations. In addition, content analysis techniques were used to analyse the data collected from the unstructured questionnaire. In this empirical study the findings highlighted two major variables that gave rise to conflicts, escalation of strikes and demonstrations at Higher Institutions of learning and recommend a conflict management style apposite for handling the conundrum. The factors dealt with in this study are not peculiar to the institution studied, but are analogous to other institutions. The findings also underscored Integrating conflict management as the most commendable style for managing conflicts at institutions of higher learning.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.043 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".