The construction of collective imaginaries through reading campaigns : – An analysis of One City One Book and Canada Reads
Bibliographic record
Abstract
This thesis examines the construction of collective imaginaries in reading campaigns, specifically the campaigns One City One Book and Canada Reads. Ten different campaigns (seven in USA; two in Ireland and one in Canada) are includedin the analysis – comprising 176 books in total. They range in time span from 1998– 2025. The research is quantitative in approach with focus on data such as author gender, ethnicity, and age. Distant reading approaches in combination with close reading are applied to analyse the summaries of the included books. The results are further analysed through a postcolonial and intersectional approach and through Bouchard’swriting on collective imaginaries. The main findings of the thesis are that its results largely mirror that of earlier studies on specifically the One City One Book programs regarding how gender, ethnicity and author age are represented. As this thesis studied later campaigns than previous research it further shows that the One City One Book reading campaign seems to have developed in quite a similar fashion across time and space in the US at least. Yet, this thesis finds that more female and ethnically diverse authors feature in this dataset than in comparison to earlier research, suggesting a tendency to move towards more gender and racial equality. Another important finding is that the representation of demographic identities in combination with the identified themes and topics suggest an important ambition to address the dark past of slavery and oppression as well as ongoing structural racism in both USA and Canada.The thesis also identifies that the collective imaginaries created through the campaigns are quite similar regardless of time and region, with focus on universal values like love, family, and identity, underscoring how we humans are more similar than dissimilar. Yet some geographical differences appear, for example the creation of the collective imaginary focusing on the American Dream in USA and that of the imaginary of place in the Dublin (in Ireland) campaign. The thesis further shows how the constructed collective imaginaries in general are more positive than the reality of the everyday societies in which they operate.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".