Contributions to professional construction in a collaborative network
Bibliographic record
Abstract
Internship is a central axis in teacher training, forcing the mobilization and articulation of different types of knowledge, both personal and professional. Becoming a teacher is, thus, an individual and collective process that requires the internal symbiosis of action, experience, and emotion, to constitute itself as a total action of a person in the performance of a specific profession. This text is based on an approach to the basic principles of teacher training, and then we proceed to make a report on teacher training courses in Portugal, in the so-called post-Bologna period, with a specific focus on internship. Given the relative autonomy of the construction and development of the courses in higher education, which we will address, we will use the master's course in Pre-School Education and Teaching of the 1st Cycle, that takes place at the Institute of Education - University of Minho, as a case from which the internship will be explained. This is realized with the support of a collaborative network, constituted by the intern, by the cooperating teacher, and by the supervising teacher, in a process of action and reflection, in which pedagogical research has a very relevant meaning.
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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".