Locating Gender Bias and Systemic Discrimination in Public Schooling Bureaucracy
Bibliographic record
Abstract
The contemporary model of bureaucracy guiding the daily functioning of public schooling in Canada is discussed in terms of its propensity for gender bias and systemic discriminatory practice. Systemic discrimination in bureaucracy is situated in a global culture of gender, founded on a universal acceptance of the duality of human sexuality, and rooted in dominant discourses of masculinity and femininity. Addressing inequities in the public schooling bureaucracy that are linked to the duality of masculinity and femininity and the resultant imbalance in the divisions of bureaucratic power and authority between men and women is fundamentally a gender issue. As such, facilitating stability-enhancing radical reform in the public schooling bureaucracy is an infinitely complex task—insofar as the foundations of the modern bureaucracy are closely tied to core sociopolitical constructs based on the so-called nature of sex, gender, and the natural distribution of knowledge-power. In the context of a critically pragmatic and practical suggestion for change, this article contextualizes Ramsay and Parker's (1992) "neo-bureaucracy" (p. 269) as a model of reform that offers the possibility for broadly defined acceptance in the teaching profession and the wider society, while bringing issues of equity and social justice to the forefront of the daily practice of teaching and learning in public schools.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".