The Limits of Role Modelling as a Policy Frame for Addressing Equity Issues in the Teaching Profession
Bibliographic record
Abstract
In this chapter, I focus on the policy implications of male teacher shortage and recruitment in terms of addressing fundamental equity issues in the teaching profession. These equity issues have been highlighted in response to the question of male teacher shortage and continue to provoke considerable debate, as I will illustrate in this chapter with specific reference to discourses about the ‘endangered male teacher’ and the policy implications of this in Canada and specifically Ontario (Abraham, 2010a, 2010b, 2010c). Concerns about male teacher shortage have been expressed in terms of two fundamental discourses or policy narratives: (i) the need for more male role models which ties in with anxieties about absent fathers and the increasing prevalence of single-parent families (Brockenbrough, 2012a; Harnett and Lee, 2003; Hutchings et al., 2008; Maylor, 2009; Pepperell and Smedley, 1998) and (ii) the question of striking a more representative gender balance in the teaching profession, a position that is underscored frequently by limited notions of equity which fail to engage with important considerations about the status of women’s work, racial inequality and male privilege (Brockenbrough, 2012b; Drudy, 2008; Drudy et al., 2005; Griffiths, 2006; Martino, 2008; Moreau et al., 2007; Riddell and Tett, 2010; Thornton and Bricheno, 2006; Williams, 1993). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.057 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.058 |
| Scholarly communication | 0.026 | 0.023 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".