Ghanaian Universities Response to Global University Rankings: Sometimes We Compare Apples to Oranges
Bibliographic record
Abstract
Global university rankings (GURs) capture the attention of university leaders, board members, and the public.A Dialogue on Asian Universities [1] webcast reinforced how university presidents employ GURs to benchmark their institutions' achievement of strategic objectives.GURs have influenced higher education policy and geopolitical discussions since their emergence in 2003 [2].Ranking schemes have become tools for students, parents, institutional leaders, and governments [3].Although GURs attracted the attention of higher education scholars who explore their impacts on students [4] and HEIs, researchers criticized these rankings for their omission of institutions from non-First World nations and an over-emphasis on research.These omissions have been addressed with more regional rankings [5], [6], [7], [8].The objective of our study was to assess how GURs are used by HEIs in the Global South.We employed a bounded, qualitative case study to explore the strategic and tactical responses of four public universities in Ghana towards GURs.Although acceptance of GURs varied by university, each reacted to GURs by adopting strategies and tactics to improve their rankings, (inter)national status, and support.Our findings showed that Ghana's public universities' institutional leaders used GURs as tools to engage in change processes in their universities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".