Bibliographic record
Abstract
The main purpose of this article is to analyse the university ranking methodologies, discusses the ways how a university may explore its identity, image and reputation, and what steps should be made upon accomplishing the self-assessment process.In order to create a maximally positive image that helps the university to achieve a competitive advantage and increase its value, the audit of the university image must be carried out. This process must be consistent and should include the evaluation of both internal and external variables. The university image audit should be regarded as part of the university strategic planning, because only consistent studies can provide information on how the university is accepted by different impact groups and what should be the trends of its creation process. Before talking about the university image audit, the university rating methodologies should be presented as they specifically provide information about how the university is seen as compared to other higher schools. The first comparative evaluation of higher schools was performed in the United States in 1983 when the journal ?U. S. News & World Report? published the first rating table of universities and colleges of the United States. Today, university rankings are drawn up and published in more than 15 countries including the USA, UK, Australia, Canada, China, Germany, Hong Kong, Nigeria, Italy, Japan, Spain, Russia and Poland. In Lithuania, no generally accepted methodology of university ranking has been created so far. On the other hand, university ranking results are only one way to find out how a university is accepted and evaluated. Analysis of the theoretical literature, empirical studies and surveys conducted by market research and consulting companies allows suggesting that information for the holistic assessment of university image may be obtained by studying its identity, image and reputation.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".