Specific competences in the Tuning Latin America Project: their degree of importance and achievement among a sample of psychology students
Bibliographic record
Abstract
The implementation of the competence-based education approach at university level is a vehicle for the global transformation of the current Higher Education system. Over the past few decades, psychology has increasingly focused on the identification of core competences in the education of psychologists. The U.S., Canada and Europe have adopted competence-based education approaches. More recently, in 2013 the Tuning Latin America Project introduced the challenge to reach agreement on the education of psychologists in the region. The purpose of this research is to analyse the degree of importance and perceived achievement of the specific competences set out in the Tuning Latin America Project, among a sample of 100 advanced psychology students of a private university in the City of Buenos Aires. For such purpose, the Specific Competences Survey for students of the Tuning Latin America Project was used. All the competences obtained high ratings in terms of importance, in particular those related to professional ethics. In addition, the respondents considered that most of the competences are thoroughly developed during their university training. To conclude, further studies and analyses need to be carried out in order to identify current educational needs for psychologists and thus enhance quality and adjust psychological practice to current social needs.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".