Exploring the Attributes, Experiences, and Motivations of Female Public Elementary School Administrators of Colour in Ontario
Bibliographic record
Abstract
EXPLORING THE ATTRIBUTES, EXPERIENCES, AND MOTIVATIONS OF FEMALE PUBLIC ELEMENTARY SCHOOL ADMINISTRATORS OF COLOUR IN ONTARIO Doctor of Education 2016 Anjili Sophia Pant Graduate Department of Leadership, Higher and Adult Education University of Toronto Abstract This study explores how the attributes, experiences, and motivations of female public elementary school administrators of colour in Ontario have contributed to their entry into administration. Data were obtained through qualitative life-history interviews with 3 currently practicing, middle-aged administrators: 2 African Canadian (a principal and a vice-principal) and 1 South Asian (principal). Resilience theory was used to investigate internal and external attributes of the participants, disruptions they faced, and motivational factors that enabled them to become administrators. Cross-case analysis revealed relevant attributes the participants shared: focus, resourcefulness, independence, social competence, a capacity for hard work, self-discipline, optimism, a positive attitude, perseverance, and a willingness to learn. Barriers to their entrance into administration were: the interview process, the struggle to obtain Canadian credentials, politics, and difficulty having their leadership skills acknowledged. Motivations to enter administration were: encouragement from others, persistence after unsuccessful attempts to enter administration, being able to meet interviewers’ expectations, and a passion for the work. Among the findings were participants’ recommendations for aspiring female administrators of colour: Keep your ambitions to yourself. Know that it is a lonely job. Understand what the work entails. Recognize that administrative positions are not powerful ones. Avoid interacting only with people from your own ethnic background. Do not use the “race card” as an excuse for not succeeding in the interview process. Remain calm when discriminated against. Most importantly, pursue the necessary credentials: Prepare for administration early in your career (work at the family of schools level, community, culture and caring, and numeracy and literacy). Seek out a variety of mentors. Stay connected with people who support you. Master a specific skill. Be resourceful. Keep current with educational jargon. Continue to develop professionally. Write and present at workshops. Use the media to showcase your school and practice. Finally, support the advancement of quality women of colour. Suggestions for future research include: (a) repeat this study in 5 and 10 years; (b) examine leadership programs and determine whether women of colour are being hired; and (c) compare and contrast the career paths of women and men (both of colour and White).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".