Hyper-diversity in Sampling Strategy for Reader Response Studies in an Urban Context.
Bibliographic record
Abstract
Early strategies of researching readers turned scholars to hermeneutic shortcuts like Iser’s ‘implied’ or Fish’s ‘informed’ reader. However, these shortcuts cannot be seen as studying ‘actual’ readers. One approach to studying actual readers has been turning to empirical methods. However, even though the institutions that do these types of research are located in culturally complex cities, the process of participant selection in empirical studies often does not take the city’s make-up into account. Therefore, this article aims to present a participant sampling strategy for empirical reader response research with Antwerp as the location for a study of urban readers in a European context. Opting for a qualitative approach and thus a purposeful sampling strategy and taking the hyper-diverse nature of major cities into account, we suggest using social milieu rather than traditional descriptive markers by recruiting from different neighbourhoods. This as neighbourhoods have their own culture and play an important role in a person’s identity. Turning to local libraries for participant recruitment means a step towards studying actual readers and will lead to a deeper insight into the effects of texts on readers. Moreover, apart from obtaining a richer variety of idiosyncratic responses, this can also result in a deeper understanding of (sub)cultural responses to narratives.
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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 | 0.178 | 0.275 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".