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Record W3167215791 · doi:10.20378/irb-49669

Party soldiers: The selection of electoral leaders in parliamentary democracies

2021· dissertation· en· W3167215791 on OpenAlexaboutno aff
Javier Martínez Cantó

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsnot available
Fundersnot available
KeywordsNOMINATENominationPolitical sciencePoliticsLegislatureIncentivePresidential systemPrimary electionGeneral electionPublic administrationPublic relationsLawEconomicsComputer science

Abstract

fetched live from OpenAlex

The selection of candidates for public office is one of the pivotal functions political parties perform in liberal democracies. Many works have studied the nomination of candidates for the legislative branch. Few studies have looked into the nomination of candidates for the executive branch in presidential democracies. However, very few works have looked at candidates' nomination for the executive branch in parliamentary democracies, who have been called electoral leaders or top candidates. This dissertation contributes to filling this gap by exploring three main research questions. First, what criteria do political parties use when nominating a top candidate? Second, to what extent do political parties nominate their top candidates on electoral considerations? Third, under which conditions is electoral competition more likely to shape party decision-making? I consider that political parties hold two criteria when nominating a top candidate. First, based on top candidates' electoral and campaigning function, parties seek to nominate electable top candidates likely to achieve more votes. Second, considering that top candidates may become prime ministers after the election and perform a series of post-electoral functions, parties will seek to nominate more reliable candidates who stay close to their party’s preferences. Building on the literature on party organization change, this dissertation proposes a new theoretical framework for understanding how different incentives can drive parties to nominate top candidates closer to one or the other criterion. In particular, this dissertation studies four factors: the party’s screening and recruitment capacity, internal demand, external demand, and the type of selectorate. To test the influence of these four factors, this dissertation presents a novel dataset of more than 2500 sub-national top candidates in Canada, Germany and Spain. There have been collecting information about the personal, partisan and political background of top candidates, which been complemented with information about the type of selectorate, the party’s internal structure, and the electorate's state. The main results are summarized as follows. First, parties are heavily dependent on their access to public institutions to recruit and train new members and produce top candidates with high degrees of reliability. Second, the results show that political parties are more reactive to changes in their internal coalitions' composition than to changes in the overall electorate. Finally, the results show that party primaries tend to differ from party conferences and party elites when the party has experienced some environmental change regarding the type of selectorate. This dissertation contributes to the understanding of the role of top candidates in parliamentary democracies and academic knowledge about party organizational change and adaptation.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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.

Opus teacher head0.058
GPT teacher head0.375
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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