Hyper-diversity in Sampling Strategy for Reader Response Studies in an Urban Context.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it