Bibliographic record
Abstract
The early childhood classroom is a space filled with contradictions. While a primary classroom is full of possibility, there exists a quiet narrative that we take for granted which serves to silence the voices of both children and teachers. This narrative is powerful in the space it occupies and within these contexts it manifests itself in a variety of ways. The goal of this article is to highlight some of the ways in which we are silenced and perhaps offer the courage to reframe our daily lives in the early childhood context. The article is inspired from a critical theory perspective, taking into consideration the issues related to power and oppression. The author believes these themes are real and very alive in our classrooms and it serves us as teachers to look critically at our circumstances so that we may begin to reclaim our voices. To deny that themes of oppression exist in our spaces is to further deny our voices, as teachers, and of those of our youngest learners. This article examines the following themes of classroom spaces and curriculum. The author observes that these themes play themselves out in her own classroom and other early childhood settings, which she believes serve to silence the voices of those who live in these spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| 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".