Generation X leaders from London, New York and Toronto
Bibliographic record
Abstract
Inspired by scholarly calls to focus more intently on the influence of context on leaders’ construction and negotiation of identity, this paper draws on evidence from our Economic and Social Research Council (ESRC) project in London, New York City and Toronto. Throughout the paper, we strive to illuminate how the city-based context influences how race/ethnicity is experienced and described. We use social identity theory, organisational fit and in-group prototypes to frame school leaders’ explicit discuss race/ethnicity when reflecting on identity. We describe our data gathering process using our Professional Identity card-sort Tool, which guided leaders’ reflections on identity. The analysis details how we extracted and interpreted evidence from leaders who were explicit about the interrelationship between their own personal racial/ethnic identification and its alignment or misalignment with their school-level communities. We explore how different city contexts influence leader experience of in-groups and out-groups and the related leadership challenges and opportunities. In conclusion, we reflect on the influence that structures, policies and communities have on how leaders experience identity and the possible implications for their work. We also explore the value of attending to potential context-based identity-driven experiences for school leader development and support.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".