Understanding the University-Sustainability Link through Media: A Spanish Perspective
Bibliographic record
Abstract
This article provides a procedure to universities for understanding the social perception of their activities in the sustainability field, through the analysis of news published in the printed media. It identifies the Spanish news sources that have covered this issue the most and the topics that appear in that news coverage. Using a probabilistic topic model called Latent Dirichlet Allocation, the study includes the nine dominant topics within a corpus with more than seventeen thousand published news items (totaling approximately five and a quarter million words) from a database of almost thirteen hundred national press sources between 2014 and 2017. The study identifies the news sources that published the most news on the issue. It is also found that the amount of news on sustainability and universities declined during the covered period. The nine identified topics point towards the relevance of higher education institutions’ activities as drivers of sustainability. The social perception encapsulated within the topics signals how the public is interested in these activities. Therefore, we find some interesting relationships between sustainable development, higher education institutions’ missions and behaviors, governmental policies, university funding and governance, social and economic innovation, and green campuses in terms of the overall goal of sustainability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".