In Praise of Talent: Meritocracy and Social Justice in theDiscourses of Grammar School Teachers: The Case of Sweden,1927-1960
Bibliographic record
Abstract
This article seeks to analyze the discourses of education and social justice articulated in Tidning för Sveriges Läroverk (TFSL), the journal of the grammar school teacher union, during the period 1927-1960.I pose two questions: how teachers defined the ideal grammar school student, and what groups of students were thereby implicitly or explicitly excluded.In order to situate these discourses historically and socially, the first section of the paper will provide a broad outline of the rise of the Swedish educational system in the nineteenth century and the way discourses of social justice affected this process.The theoretical framework used for the analysis is inspired partly by Foucault's genealogical method for discourse analysis and partly by Pierre Bourdieu's theories of capital and field. 2 I have also used Joan W. Scott's model for the analysis of gender in order to understand how the social category of the grammar school student was constructed in the discourse of the grammar school teachers.3 Book Reviews/Comptes rendus 95 4 As will be shown later, it is correct to use the pronoun "his," not "her," in this context.5 Ulla Johansson, "Historien om likvärdighet i svensk skola," in Likvärdighet i svensk skola: En antologi (Stockholm: Skolverket, 1994).6 In this respect there are striking similarities among many countries, even though different national contexts were also reflected in the educational systems.Cf. e.g.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.032 | 0.061 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 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".