Formarse en la Investigación Educativa: una Comunidad de Pensamiento en Torno a la Escritura de la Tesis Doctoral
Bibliographic record
Abstract
This article explores educative experiences we had as doctoral students in a community of knowledge inside the University of Barcelona. We deepen understandings around the process we lived for five years, as well as we point out theoretical and methodological aspects that framed the process itself. Embracing narrative inquiry as methodology, we enhance the need of shifting some of the doctoral training practices that traditional academic systems still hold. Through our stories, we show bumps and tensions that might emerge in living and working in a community of knowledge. We also raise challenges that beginning researchers are currently facing in the educational landscape. Those challenges are related to the ways in which we inquire, approach, attend to, and name the research experience in the context of increasingly high academic demands.
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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 | 0.052 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".