International research on accreditation as a strategy for educational quality: identification of patterns in scientific collaboration
Bibliographic record
Abstract
Purpose This study aims to identify the patterns of scientific collaboration in the published literature on academic accreditation processes. Design/methodology/approach The methodology was based on a non-experimental approach using the quantitative method. A non-probabilistic sample covering 1937–2024 in the Scopus database was delimited. Bibliometric indicators of scientific collaboration, such as collaboration networks of authorship, institutions and countries, were calculated, represented and analyzed. Scientific maps were generated for visualization. Findings The results of this study reveal a highly centralized global collaborative network, particularly among countries and institutions in the northern hemisphere, such as the United States, Canada and several Western European nations. This can be attributed to each country’s scientific capabilities, access to resources and internationalization policies. On the other hand, it also reflects an asymmetry in the global generation of scientific knowledge, where countries and institutions with less scientific development continue to hold a peripheral role. Originality/value Analyzing the patterns of scientific collaboration on accreditation processes has comprehensively revealed the main trends, schools of thought and topics associated with the socio-intellectual structure of academic accreditation research.
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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 | 0.062 | 0.190 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.026 | 0.056 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".