Bibliographic record
Abstract
Chapter 1 Introduction 1.1 Census-taking and EU enlargement in the Western Balkans 1.2 Research question(s) 1.3 Europeanisation 1.4 Methodology 1.5 The paradox of collecting ethno-cultural data in censuses 1.6 Europeanisation of census-taking in the Western Balkans 1.7 State capacity, domestic and external actors 1.8 Contributions & relevance 1.9 Outline of the thesis Chapter 2 Counting for enlargement? The theory behind the Europeanisation of census-taking Abstract 2.1 Introduction 2.2 Europeanisation 2.3 Europeanisation in enlargement countries 2.4 Compliance with the EU census regulations 2.4.1 The census requirements 2.4.2 Measuring compliance with the EU census regulations 2.5 Embedding Europeanisation in the chapters 2.5.1 Europeanisation of census-taking and the paradox of collecting ethno-cultural data 2.5.2 Conditionality and legitimacy of the Europeanisation of censustaking 2.5.3 State capacity, domestic and external actors 2.6 Conclusion Chapter 3 Using a mixed methods research design while studying census-taking in the Western Balkans Abstract 3.1 Introduction 3.2 Mixed methods: potential designs and selected model 3.3 Illustrative study 3.4 Research design 3.5 Qualitative data collection: In-depth interviews 6 3.6 Quantitative data collection: Expert survey 3.7 Discussion and conclusion Chapter 4 Counting for what purpose? The paradox of including ethnic and cultural questions in the censuses of Croatia, Bosnia and Macedonia Abstract 4.1 Introduction 4.2 The sensitivities of census-taking 4.3 Europeanisation and census-taking 4.4 Expert perspectives on counting ethnic and cultural characteristics 4.5 Ethnic and cultural categories in Croatia, Bosnia and Macedonia 4.5.1 Croatia 4.5.2 Bosnia and Herzegovina 4.5.3 Macedonia 4.6 Comparison 4.7 Conclusion Chapter 5 'When counting counts': Europeanisation of census-taking in Croatia, Bosnia and Macedonia Abstract 5.1 Introduction 5.2 Census-taking within Europeanisation 5.3 Europeanisation of census-taking: Conceptual framework 5.3.1 Conditionality 5.3.2 Legitimacy 5.4 Method, case selection and data 5.5 When counting counts: Case analysis 5.5.1 Bosnia and Herzegovina 5.5.2 The Former Yugoslav Republic of Macedonia 5.5.3 Croatia 5.6 Conclusion Chapter 6 Census-taking in the Western Balkans: A matter of state capacity or the influence of domestic and external actors? Abstract 11 voice in my head provided me with the much needed strength to complete this project.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".