“I THINK, SHE DOES NOT KNOW THIS ANY MORE”: CHILD AGENCY NEGOTIATED THROUGH THE DYNAMICS OF FAMILY INTERACTIONS IN GERMANY DURING COVID-19
Bibliographic record
Abstract
We examined child agency in terms of prevailing theories of childhood (seeing children as “been”, “being”, or “becoming”) in the context of interviews about COVID-19 experiences. In our study we focused on conversations between the interlocutor (a child or a parent) and additional family members and used a discourse approach for analyzing and interpreting the interactions. In total, 9 families of diverse sociocultural backgrounds (13 parents and 16 children aged 6–15 years) were interviewed about their individual COVID-19 experience in Germany. Irrespective of age, children were able to describe events and experiences during COVID-19 times and were thus aware of the “been”. These ranged from enjoying playtime with parents to playgrounds being cordoned off and classmates being ill. Interview interactions underscored a dynamic of children’s agency that alternated between active involvement in their own and the parent’s interview and reactively referring questions to a parent or letting the parent take control of the interview situation. Neither parents’ views of children nor children’s own behavior could be consistently assigned to the category of “being” or “becoming”. Rather, our study highlighted children’s agency along a being–becoming continuum through an interactive transformational process in terms of interdependent agency between children and parents in a particular context. This process of negotiated agency should be explored further in future studies with children and parents.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".