Value generation from academic activities in a public higher education: A lean perspective
Bibliographic record
Abstract
Abstract The concept of value, in a context of higher education institutions (HEIs), simply refers to meeting or exceeding customer requirements and expectations. HEIs have a fundamental role in the dissemination of knowledge, in addition to developing new skills and awareness for future professionals, in relation to local, regional and national issues. The aim of this research is to analyse the academic activities and outcomes influence on value generation perception in a public university students' perspective. A survey, built from a structured questionnaire, was carried out in a Brazilian HEI, the Federal University of Santa Maria, with students who are in their course final stage. Data collection came out with a valid sample of 389 respondents. It was analysed using descriptive statistics, exploratory factor and regression analyses. The findings suggest that among the services and resources that the university may offer to its students, the ones that present the most impact refer to cognitive and social abilities emerging from specific teaching practices and strategies from the professors. This research presents useful insights and applicable elements from which public managers can utilize to enhance engagement, create value and build a more rigorous and relevant practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".