The professionalization of election campaigning in Mexico: a study on continuity and change in presidential campaign practices and communications (1988-2006)
Bibliographic record
Abstract
This thesis seeks to fill a gap in the literature on Mexican politics relating to the analysis of major recent changes in campaign tactics and strategies (usually referred to as ‘campaign professionalisation’) and their causes. I argue that the Mexican experience may shed light on the factors driving the professionalisation of electoral campaigns in new democracies, particularly on the causal role of a number of systemic and party-level variables. Building on the comparative literature on party and campaign change, and using Qualitative Comparative Analysis (QCA), this study shows that the professionalisation of presidential campaigns in Mexico was not only driven by the demands of large-scale changes in the Mexican party and media systems during democratisation on candidates’ campaign organisations, but also, to a significant extent, by a number of parties’ organisational features and resources. The argument of the thesis is that while party-specific factors are not the ultimate causes of campaign innovations, they are key mediating conditions between broader systemic changes on the one hand, and campaign behaviour on the other. They are therefore crucial in order to explain cross-party differences in the extent of the adoption of professionalised campaigning in Mexico.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".