LET US TALK ABOUT INTERNATIONALIZATION OF HIGHER EDUCATION: SMALL INITIATIVES THAT MAKE A DIFFERENCE ON CAMPUS
Bibliographic record
Abstract
This is a descriptive, exploratory and applied study about the perception of professors and graduate students on the priority of internationalization programs and actions on campus. Reflections about the internationalization of higher education presuppose that a world framed by language barriers encompassing two fields of knowledge should be overcome: that of education and language. Language barriers represent a challenge to the academic community and the complexity of university management prevents Institutions of Higher Education from assuming new pedagogical commitments. The present investigation proposal is theoretically scaffolded by Jurgen Harberma’s theory of communicative action, which is based on the action of communication and suggests a comprehension of acts in a mutually understanding direction. The data were collected among master’s and doctoral students in a private university of the Federal District. The data analysis, which consisted in an application of descriptive statistical techniques and cluster analyses, was carried out through the SPSS-22 software. The results demonstrate that 71% of master’s students consider that initiatives to offer foreign language extension courses should be a priority on campus, while 75% of doctoral students indicated the importance of international events and language-practicing conversation groups.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".