What, how, and why to mix research methods in the study of career patterns?
Bibliographic record
Abstract
Political careers have become more diversified in multi-level systems over the last decades. In ‘classic’ federations (e.g. the US, Canada, and Germany), regional offices have become attractive positions with the process of professionalization. In newly regional political systems (e.g. Belgium, Spain, and the UK), the regional level has been quickly perceived by ambitious candidates as a professionalized political arena. Overall, regional positions in multi-level contexts are no longer conceived as amateur positions or mere stepping stones towards the national level. Despite the growing literature on the topic, several methodological questions remain nonetheless opened. This paper discusses the benefits and limitations of a mix-methods research design relevant for the study of elites’ career patterns. Specifically, the paper presents how and why mixing two quantitative and qualitative methods: survival analysis and life story interviews (for the purpose of illustration, the paper relies on empirical data: 1.831 careers and 84 life stories). Firstly, I introduce separately the added-value of each research methods (I especially discuss the benefits of these approach to collect and analyze longitudinal data in a context of multi-level system). Secondly, I demonstrate how these two methods permit to better understand elites’ career patterns in the context of multi-level systems.
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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 | 0.428 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.027 | 0.037 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".