What political science can learn from the humanities: Blurring genres
Bibliographic record
Abstract
This book asks, ‘what are the implications of blurring genres for the discipline of Political Science, and for Area Studies?’ It argues novelists and the playwrights provide a better guide for political scientists than the work of physicists. It restates the intrinsic value of the Humanities and Social Sciences and builds bridges between the two territories. The phrase blurring genres covers both genres of thought and of presentation. Genres of thought refers to such theoretical approaches as post-structuralism, cultural studies, and especially interpretive thought. Part 1 explores genres of thought, focusing on the use of narratives. Specific examples include the narratives of post-truth political cultures; narratives in Canadian general elections; autoethnography as a new research tool; and novels as a way of understanding economic development. Part 2 emphasises genres of presentation and focuses on the visual arts. The chapters cover: photography in British political history, the architecture of American statehouses and city halls, design, comics, and using the creative arts to improve policy practice and theory.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".