Greening and Energy Issues: An Analysis of Four Canadian Newspapers
Bibliographic record
Abstract
The concept of greening has been part of the public discourse for some time. The greening of energy, which is seen as essential for green growth and a green economy, which in turn is seen as essential for sustainable development, is part of this public discourse. Media is seen to be important in shaping public opinion. We investigated how two national Canadian newspapers (Globe and Mail and National Post) and two regional (Alberta) newspapers (Calgary Herald and Edmonton Journal) cover greening as it relates to energy issues. The key findings were: although 88% of newspaper articles that covered greening also covered energy issues, only 0.15% of the articles that covered energy issues also contained the term greening; greening was mostly framed in economical not environmental protection terms; many themes were covered but often only once in a given article and often in less than 10% of the overall articles. This analysis could give guidance to stakeholders such as industry and non-governmental organizations on how greening is discussed in relation to energy issues. The analysis suggests that there is a need for increasing the visibility of greening in energy covering articles, that a more analytical approach is needed linking it for example to the greening indicator discourse as indicators are envisioned to give guidance as to what to do how and that a rethinking of how stakeholders distribute their greening message as it relates to energy issues might be warranted.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.028 | 0.057 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".