COVID-19 and Youth Living in Poverty: The Ethical Considerations of Moving From In-Person Interviews to a Photovoice Using Remote Methods
Bibliographic record
Abstract
COVID-19 hit and instantaneously research using in-person methods were paused. As feminist and critical social work scholars and researchers, we began to consider the implications of pausing our ongoing project exploring the provisioning and resilience of youth living in low-income, lone mother households. Reflexively, we wondered how the youth, families, and issues we were connected to would be impacted by the pandemic. We were pulled into both ethical and methodological questions. While the procedural ethics of maintaining safety were clear, what became less clear were the relational ethics. What was brought into question were our own social positions and our roles and responsibilities in our relationships with the youth. For both ethical and methodological reasons, we decided to expand the original research scope from in-person interviews to include a photovoice to be executed using online, remote methods. In this article, we discuss those ethical and methodological tensions. In the first part, we discuss the relational ethics that propelled us to commit to expanding our work, while in the second part, we discuss our move to combining photovoice and remote methods.
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.
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 | 0.197 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.028 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 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".