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Record W2341231607 · doi:10.14288/1.0055631

The life history of a First Nations educator : never too old to learn

2009· article· en· W2341231607 on OpenAlexaboutno aff
Maisie Cardinal

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryPsychology

Abstract

fetched live from OpenAlex

This dissertation begins with the loss of my immediate family and search for healing. I share my life experiences because I want to move beyond the pain and suffering but also because I want to leave a written monument to honour the memory of my family. My research purpose for writing my Indigenous life history focussed on my search for healing; correcting stereotypes of Aboriginal women and Aboriginal students; understanding the impact of major federal government policies upon Aboriginal people; and examining the role of Aboriginal parents in their children's education. I chose the Indigenous life story methodology because it allowed me to remember and be reflective about critical incidents of my life stages: childhood, adulthood, motherhood and educator. These Indigenous life stories exemplified the relationship between an Aboriginal family and the wider Canadian society regarding the impact of major federal laws and educational policies. The Indigenous life story methodology also challenged me to critically explore the following research topics/questions: ways of healing from deep emotional pain; overcoming stereotypes about Aboriginal women and Aboriginal learners; examining parental involvement; and facilitating Aboriginal student success. My search for spiritual and emotional healing has led me to face the difficult challenges with the assistance of others. When elders mentioned that I must return to Nanaimo, my birthplace, I followed their guidance. I returned and visited my parent's graves every year for four years. With the assistance of my paternal uncle and later with my maternal aunt, I was able to complete this task. On the fourth year, I walked the land where our home once stood with my maternal aunt. Even though this experience was difficult, I remember thinking that I could not change the path of my immediate family but I had to accept their passing. Besides discussing the loss of my family, I included my children's and my educational experiences to illustrate that First Nations children and adults are often stereotyped as being inferior and therefore, not able to achieve at the same level as mainstream students. One of my sons was told that "he was only average and would not amount to anything." No matter how hard he worked, he was only average. The teacher had decided where he felt my son's achievement level was and maintained this academic grade. Today, he is a lawyer with a Master's in Law. This dissertation includes one story from each of my children plus my own experiences with education. As an educator I emphasize the importance of Aboriginal identity. Students need to be able to identify with their Aboriginal or Native background in order to meet the challenges of post-secondary education. Once the students make the effort to identify with their Aboriginal heritage, they are able to focus their full attention on their academic studies and not be afraid of someone revealing Aboriginal identity. In conclusion, I used the story of unpacking a suitcase of all of the negative experiences of hurt, sadness and real life experiences of physical, sexual, and psychological abuses, so that our communities will start to heal and look toward more positive and healthier approaches to life for our children, grandchildren and great, great grandchildren.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0250.012
Scholarly communication0.0090.009
Open science0.0010.006
Research integrity0.0020.009
Insufficient payload (model declined to judge)0.0090.003

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.

Opus teacher head0.011
GPT teacher head0.209
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

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