Engaging Young Fathers in Research through Photo-Interviewing
Bibliographic record
Abstract
Although conducting interviews is the most popular research strategy in qualitative research, we question whether it is the best strategy to use with young fathers and other populations who may be less willing to share personal experiences and thoughts with an unknown researcher. The reluctance of young fathers to engage in research leads to the omission of important perspectives and inadvertently results in young fathers' being understudied and unwittingly excluded from support programming and services. In this paper, we describe our experiences of using two different research strategies with young fathers: conventional in-depth interviews (i.e., interviews that rely on words only) and photo-interviewing (i.e., using photographs as props during an interview). We found that photo-interviewing contributed to young fathers' comfort during the research process, provided them a sense of agency, and possibly enriched the quality of the data. While we do not argue that one data collection strategy is necessarily better than the other, we would like to caution researchers against using conventional interviews as a default data collection strategy with marginalized, vulnerable, or less verbal populations for whom interviewing may not be the most suitable data collection strategy and to encourage researchers to explore alternative options.
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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 | 0.033 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".