“You Didn’t Have to Pay Me”: The Meanings of Monetary Incentives in Interview Research
Bibliographic record
Abstract
This paper explores the social meanings of monetary research incentives and the ramifications of their use in interview research. I argue that monetary incentives produce complex social meanings that significantly and diversly shape the relationship between interview researchers and participants and, as such, impact the types and volume of data that interviews produce. I discuss three distinct social meanings that emerged in my qualitative research interviews with forty-four low-wage freelance refugee interpreters in Canada. First, I show that in research with low-income workers, incentives can be interpreted as symbols of cross-class allyship that place the researcher in trusting and highly cooperative relationships of solidarity with participants. Second, the use of research incentives may also, and somewhat paradoxically, deepen socio-economic hierarchies by placing researchers and participants in relations resembling those between employers and employees. Third, research incentives may also be used by participants to resist social hierarchies and establish relations of benevolent and charitable equivalence in the interview encounter. Thus, the various social meanings of monetary incentives are productive of distinct interpersonal dynamics that shape the process of data collection as well as recruitment.
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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 | 0.185 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.016 | 0.074 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".