Sustainability training at Spanish public faculties of education: a virtual ethnography
Bibliographic record
Abstract
Various institutional statements and official documents refer to the Sustainable Development Goals and sustainability training. This should imply a university education in these values, considering that those who are going to train the new generations, in the Spanish State, must pass through the faculties of education. Through an investigation developed thanks to the methodology and design of virtual ethnography, the web pages of all the Spanish public faculties of education were analyzed, managing to find few training options during the initial period of the 2020/2021 academic year, specifically during the months of September and October, in a reality that reflects deficiencies that should be corrected immediately. Education for sustainability cannot be a priority in the 2030 Agenda and yet, in future education professionals, remain in a declaration of intentions, or something that implies little more than some isolated training course, and the results of the research show that the object of study barely manages to be found in the training offered. In the same way, being able to find training for education in sustainability should not involve an arduous research within the official websites, since this would not reflect a priority approach on the part of the institutions.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".