Science without Borders – An alternative framework for evaluation
Bibliographic record
Abstract
This paper proposes a tentative methodology to critically evaluate some aspects of the first phase of the Brazilian international higher education mobility program called Ciência sem Fronteiras (Science without Borders SwB) on its undergraduate scholarship share in Canada. The data to be analyzed come from the first Calls (108/2012-109/2012), and three of the monitoring tools designed by the program administrators: the immediate, straightforward data available on the Painel de Controle and Bolsistas pelo Mundo sites (prepared by the program administrators themselves) and expanded with the further information provided (or not) by the students in their Lattes CV (an electronic standardized record of academic life). For this study, we selected the total of 522 students who were placed in the five Canadian universities which presented the largest number of SwB undergraduate students all through the first phase, spent their period abroad and returned to complete their courses in their home universities in Brazil at least 21 months before – at the time data were collected (January, 2016). Our understanding of educational policies is in line with The Policy Cycle Approach (Bowe, Ball and Gold, 1992). The concept of ‘disposition’ (Dewey 1992; Andreotti et al. 2015) will be used as a framework for a critical analysis of the attitudes students have related to updating their Lattes CV, which is here understood as a tool that enables accountability to stakeholders.
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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 | 0.275 | 0.269 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier 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".