The Research Persona Method: Figuring and Reconfiguring Personalised Information Flows
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
Abstract This chapter explores the prospects of assembling “research personas” as a qualitative method that uses the positionality of the researcher situated amidst algorithmically suggested content for studying how digital personalisation is produced, encountered and experienced. Building on past and ongoing digital methods projects concerning the networked flow of misinformation, disinformation and authenticity, the research persona method looks not only inside but also across algorithms and digital methods approaches that repurpose the methods of the medium. As an immersive method that is both a product and mode of studying “figuration”, configuring research personas can show how personalisation is produced and accomplished by the interplay of various actors, devices, interfaces, infrastructures, user practices and data flows.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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