Cultural Capital and Community in Contemporary City-wide Reading Programs
Bibliographic record
Abstract
There are currently more than 500 city-wide reading projects in the US, and dozens in Canada and the UK. Through creative and traditional programming, such as canoe treks and book discussion groups, producers often use the One Book, One City model to “create community” through a selected text. This essay argues that instances of coming together to share reading experiences can be considered literary cultural fields as the French sociologist Pierre Bourdieu conceived them. Readers seek cultural capital by participating in events because participation in book culture is considered a commendable and valuable activity. However, in order to participate, one needs to already have a certain amount of cultural literacy and capital. The essay offers an analysis of readers’ articulations of why they do and do not participate in city-wide book programming to help us better understand the motivations, pleasures and obstacles of membership in ephemeral reading communities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.025 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".