Tomma ord och övergivna löften : En redogörelse om de rödgrönas miljö-och klimatpolitiska löften och genomförande
Bibliographic record
Abstract
Election promises and their fulfillment are subjects that have been studied quiete extensively within the academics, this thesis seeks to investigate how the promises given by the red-green block in Sweden during the mandate periods of 2014-2018 and 2018-2022 have or have not been fulfilled. The theoretical basis used in this thesis is that of Rational choice with Gunnar Sjöbloms three arenas that parties engage in and that of the responsible party model. In this study the method used is a theory consuming one, this type of method uses multiple theories on the same case and investigates how well each of them can explain the phenomena. The promises were taken from each of the parties manifestos from each mandate period and following this some were left out since they could not be fulfilled with national level policies. The results of the study showed that mandate period 1 followed previous results and studies quite well whilst mandate period 2 results differed from previous results. A number of factors could help explain this vast difference and with these variables accounted for the results became more in line with previous studies results. The study showed that for the most part parties do keep most of their promises to some degree but how these studies also must be conducted on a case by case basis to achieve the best results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".