‘I’m going to call my friend to join us': connections and challenges in online video interviews with children during COVID-19
Bibliographic record
Abstract
This paper explores research with children through repeated online video interviews during the COVID-19 pandemic. It provides insight into the geographical and relational affordances provided by online interviewing, including repeated online interviewing, and discusses some of the kinds of unpredictability that can uniquely arise during online interviews. We draw on a qualitative research study conducted with children in Ontario, Canada, that explored their early pandemic experiences. With attention to children’s participation, knowledge production and relationality, we reflect on the challenges, advantages and unexpected ethical moments that arose through using online video interviews. We provide a comprehensive reflection on our longitudinal, online research with children during a global crisis by focusing on three areas: building relationships in online interviews; entering and exiting children’s worlds; and unexpected ethical challenges of online interviewing. Within these three areas we provide insight into relational dynamics shaped by the online space, how online video interviews with children can provide opportunities for them to share their feelings, the importance of careful planning when exiting research projects, and how online engagements provided relational spaces for understanding, building rapport, finding comfort, listening, and sharing during the early days of COVID-19.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".