Bibliographic record
Abstract
Chapters 6 and 7 analyse the perceptions of actors in the policy-making process with regard to EMU. As was mentioned in Chapter 1, the data discussed in this study are mainly based on interviews, but supported by ‘official’ sources (for example annual reports, press releases) as well as by secondary sources including newspaper articles. Before discussing the data provided by the interviews in the next chapter, the present chapter addresses the methodological questions related to the choice of using interviews as the basis of the present study. This chapter is structured as follows. In Section 5.1 a brief justification is given as to why the research is carried out using a qualitative approach rather than a quantitative approach. Section 5.2 discusses the problems related to using interviews as a mode of data collection, and examines the question whether or not those problems distort the aim of the present study. In the last two sections the method used for the present study is described. Section 5.3 discusses the political context in the two periods in which the interviews were conducted. In addition it explains how the respondents were selected. Finally, in Section 5.4 the questionnaire that was used for the interviews with the respondents is provided and the aims of the questions are explained. A very short summary is provided in Section 5.5.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.265 | 0.069 |
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".