Bibliographic record
Abstract
This study examines the information behaviours of Black romance readers seeking books with Black character representation. With previous literature on systemic racism in romance publishing providing context for this study, the aim is to identify Black romance readers’ information-seeking habits. Racialized differences stemming from the publishing industry to the accessibility of these books create challenges that Black readers must navigate, thus producing differences in information behaviours. Therefore, this study provides insight into the underexplored subject of Black romance literature. The population of this research is Black romance readers who read for pleasure and consider themselves avid readers of Black romance novels. Using the methodology of a semi-structured interview followed by the drawing of an Information Horizon Map, interviews were conducted with three participants, yielding insightful results. An inductive thematic analysis of the findings produced four common themes: (1) in-depth searching practices, (2) customized cataloguing resources, (3) using covers as cues, and (4) community advisory. These findings demonstrate that Black readers are willing to conduct extensive search practices but do so with the help of other Black readers. Findings also display the dedication of readers in pursuing representative stories of Black love. Situating these findings within popular models of information behaviours, this study draws attention to a fascinating population that is understudied in library and information science literature.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".