MAKING A DIFFERENCE IN THE LIVES OF YOUNG CHILDREN: A CRITICAL ANALYSIS OF A PEDAGOGICAL DISCOURSE FOR MOTIVATING YOUNG WOMEN TO BECOME EARLY CHILDHOOD EDUCATORS
Bibliographic record
Abstract
Findings in this article indicate that training programs use a key pedagogical and ideological discourse of “teachers make a difference” to motivate female early childhood education students to enter and stay in the field. However, research in the area of workforce retention maintains that many graduates are not willing to enter and stay in a workforce characterized as economically, socially, and politically marginalized, and part of a secondary labour market. This article, which presents an alternative pedagogical discourse to account for the realities of the work, could initiate changes in professional identity formation, social relations, and economic arrangements. Key words: training, workforce, marginalization retention Cet article résume les conclusions d’une recherche selon lesquelles des programmes de formation se servent d’un discours pédagogique et idéologique du slogan : « les enseignants font la différence » en vue d’inciter des étudiantes en éducation de la petite enfance à s’engager dans ce domaine et à y rester. Or, des recherches sur le maintien de l’effectif démontrent qu’un grand nombre de diplômées ne veulent pas entrer et à rester dans un domaine qui regroupe une main‐d’œuvre marginalisée économiquement, socialement et politiquement et faisant partie d’un marché secondaire de l’emploi. Cet article, qui présente un autre discours pédagogique rendant compte des réalités du travail, pourrait favoriser l’introduction de changements dans la formation de l’identité professionnelle, les relations sociales et les modalités économiques. Mots clés : formation, main‐d’œuvre, marginalisation, maintien de l’effectif
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.034 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".