Unearthing ‘the real world’: A sociocultural exploration of quality in early childhood learning and care
Bibliographic record
Abstract
Early childhood learning and care (ECLC) is commonly misunderstood and undervalued in North America, where neoliberal discourses and adult-centric perspectives can overlook the epistemological and ontological richness of early childhood.This thesis investigates how adults construct 'quality' in ECLC and how sociocultural context shapes this construction.Using Sweden as a comparative context, this thesis asks: How is quality constructed in Swedish ECLC, and what is revealed about the influence of sociocultural context on educational values and practice?A secondary qualitative analysis was conducted on two Swedish studies representing parent and teacher perspectives.Reflexive thematic analysis generated three themes: 1) nesting dolls, which examines identity and temporality in adult decision-making; 2) follow the leader, which explores hegemonic pressures and social optics; and 3) the hero and the scapegoat, which demonstrates how dichotomous thinking and acts of comparison shape adult perceptions of quality.Findings show that quality construction is a relational process shaped by the interplay of imagined futures, discursive norms, and adult notions of identity and responsibility.This study contributes to ongoing discussions about how adult values influence early learning environments and challenges dominant discourses that associate quality with standardized metrics focused on future productivity and success.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.045 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| 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".