Recognizing politics in the nursery: Early childhood education institutions as sites of mundane politics
Bibliographic record
Abstract
In his inspirational article titled ‘Bringing politics into the nursery’, Peter Moss argues for early childhood institutions to become places of ‘democratic political practice’. In this article, the authors add to Moss’s call and argue that these institutions are sites of ‘mundane political practice’, containing various attitudinal orientations and ideologies, and including many kinds of purposive activities. Recognizing different dimensions of political life in institutional spaces where children lead their lives requires a differentiation between two types of politics: first, official politics and policies that aim to institute certain ideals in early childhood education and care and, second, everyday politics unfolding in communities that involve people as political subjects from birth until death. When the latter is discussed in early childhood research, if at all, it is rarely identified in political terms, which the authors consider problematic. The lacking recognition of mundane politics denies important aspects of children’s agency, which is prejudicial in itself. Moreover, such ignorance may lead to unintended consequences in democratization processes, like the one suggested by Moss. Imposing political ideals without recognizing children’s existing political agencies carries a risk of interfering with their political lives so that some children may feel misrecognized or find their capacities to act hindered or their activities misunderstood. In order to avoid such outcomes, this article is an argument for research and pedagogies that acknowledge and scaffold children’s political agencies at large.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".