OAEM OS IMPACTOS DO APOIO INICIAL NA TRAJETÓRIA ACADÊMICA DOS DISCENTES DO DEPARTAMENTO DE ENGENHARIA DE MINAS DA UFOP
Bibliographic record
Abstract
At university, when a student enrolls in an engineering course, concerns may arise immediately about the new challenges they will face, including fears about the new social environment, new responsibilities, and challenging foundational education courses.It is precisely in this initial phase of undergraduate studies that students may feel unsupported, leading to high dropout rates that can be avoided by providing guidance and initial support.In the Department of Mining Engineering (DEMIN) at UFOP, the academic orientation project for Mining Engineering (OAEM) was created in May 2013, with the aim of welcoming freshmen to the course and assisting them throughout the first semester, providing meetings that cover various topics, including the opportunities that the university and department can offer, as well as conversations and lectures with faculty members and alumni.As a result, over its 10 years of operation, the OAEM has proven to be a crucial project in combating dropout rates, positively impacting its participants, while seeking improvements to provide even more comfort for the difficulties faced by new students.
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 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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; both teacher heads agree on what is shown here.
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".