Border Temporalities of Early Childhood: Diverse Education and Care Arrangements of Cross-Border Commuting Parents
Bibliographic record
Abstract
Based on the distinction between times in childhood and times of childhood, this paper examines the border temporalities of early childhood education and care in the cross-border Greater Region, SaarLorLux. Using a practice-analytical approach to times and borders, and on the basis of qualitative interviews, two types of time-related practices are identified that parents with daily work commutes from Germany to Luxembourg carry out to set up and maintain their children’s education and care arrangements (ECAs): rhythmizing and navigating. How borders and childhood times interweave in these activities is presented along three contrastive patterns of ECAs, which demonstrate the different ‘border experiences’ that cross-border commuting parents make during their use of public services of early education and care (ECEC) in the Greater Region. This not only makes the field of ECEC its own arena of border (dis)integration, but also points to early childhood-specific border temporalities. Building on this, the findings point to the need to expand current inequality-oriented perspectives on border regions and border mobility to include the aspect of childhood and care-related border temporalities. Keywords: borders; time; border temporalities; childhood; early childhood education and care; childcare; cross-border mobility; borderlands.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".