The Global Politics of Census Taking
Bibliographic record
Abstract
This book examines in detail the state of the art on census taking to spark a more vivid debate on what some may see as a rather technical – and hence uncontroversial – field of inquiry. Against the backdrop of controversy between instrumental and performative theoretical stances towards census taking, it analyses the historical trajectories and political implications of seemingly technical decisions made during the quantification process by focusing on the 2020 round of censuses, which have been particularly revealing as activities have been affected by the ongoing COVID-19 pandemic and the ensuing containment policies. Through case studies of countries from the Global North and the Global South, the book highlights the consequences of, and innovations and challenges in census taking focusing on three particular areas of concern – the politics of the census in terms of identity politics; the institutional autonomy of the census; and significant and transformative methodological innovations. This book will be of key interest to scholars, students and practitioners of quantification studies, and social demography and more broadly to public policy, governance, comparative politics and the broader social sciences. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution‑Non Commercial‑No Derivatives (CC‑BY‑NC‑ND) 4.0 license.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".