Att uppfattas som legitim i klimatomställningen : Medias roll för en aktörs legitimitet
Bibliographic record
Abstract
The aim of the study was to see how the media affects the legitimacy of an actor in the climate transformation, by looking at a specific actor. A qualitative analysis has been carried out in the form of a document study of newspaper articles concerning an actor in the climate transformation. The qualitative analysis is supplemented with a quantitative analysis, with the aim of providing an overview of the empirical evidence. With the help of an analysis tool, we have investigated how the media affects an actor's legitimacy. The analysis tool was based on literature on how legitimacy can be created in polycentric governance arrangements, as well as within the theory of framing and from a business networks perspective. The quantitative analysis is presented in descriptive statistics and the qualitative analysis is presented in text and with a final summary in tabular form. The analysis showed that the actor often was portrayed as important in the climate transformation, but also credible in terms of empirical credibility, credibility of the speaker and social and political trust. The actor also portrayed themselves as very confident. They were also shown to care about their employees, future generations, and society at large, which should further affect their credibility and trust. Interesting findings from the analysis included bombastic rhetoric from a politician, challenges and criticism did not seem to damage the actor's legitimacy and that the actor has "skipped" some of the factors that the literature describes create or promote legitimacy. But also, the actor was described as a "done deal" early in the decision-making process and that trust in various forms seems to be the most important factor for legitimacy. The conclusions are that the portrayal of the actor in media, positively affects their legitimacy.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".