Who participates in research, and why? A response to K. M. Kim & E. Chen’s “Toward research inclusivity in applied linguistics: A reflection and methodological guideline for inclusive online experimentation”
Bibliographic record
Abstract
As Kim and Chen have shown, online outreach and experimentation have been somewhat effective strategies for reaching out to and recruiting populations not typically found on university campuses and other research hubs. In this response, I hope to expand and complexify the conversation by considering the following questions: Who participates in research, and why? In the first section, I draw on Bourdieu (Bourdieu & Wacquant, 1992; Grenfell, 2014) and argue that research is a kind of social practice emerging from the dialectic alignment between individual habitus and the social field of research. I posit that, unless certain aspects of the field of research change, some people remain unlikely to participate in research. In the second section, I discuss the critical and ethical ramifications of using social media networks and crowdsourcing platforms like Amazon Mechanical Turk and Prolific to recruit research participants. I argue that, by shifting the practice of research participation from the field of research to the fields of social media and gigified capitalism, new logics are introduced that threaten concepts that are vital to the ethical generation of valid data through research, including participant wellbeing and voluntary consent.
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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 | 0.061 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.033 |
| Scholarly communication | 0.017 | 0.032 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.054 | 0.077 |
| Insufficient payload (model declined to judge) | 0.008 | 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".