Canadian First Nations Women Preservice Teachers' Experiences and Perceptions Regarding Technology
Bibliographic record
Abstract
The primary purpose of this research was to collect data for Canadian First Nations educators and policy makers to use in making decisions surrounding issues of First Nations women and technology education. Nine First Nations women preservice teachers at the intern stage of their Indian Teacher Education Program at the University of Saskatchewan were engaged in indepth interviews concerning their experiences and perceptions regarding technology. The study found that the participants defined technology first and foremost as computer-related. Some viewed technology from the cultural aspect, and thought technology used for financial gain would take away from traditional family values. The participants thought that women needed technology training and that they needed to develop self-confidence and become role models in order for First Nations women to exercise leadership in the field of technology. The participants stated that their university experience was responsible for most of their learning about technology. They did not, however, feel prepared to face the technology they would encounter in schools. Intimidation, stereotypes, the lack of access and exposure to technology, the lack of a good self-image, lack of time, and lack of role models were perceived to be some of the biggest barriers to First Nations women learning about and using technology. Men in their use of intimidation and stories with negative images of women and technology were perceived as one of the strongest deterrents to First Nations women advancing in the area of technology. Findings from this study had significant implications. First Nations teacher preparation programs should include required credit courses and establish daycare centers. Band controlled schools should update computers and make provisions for technology education by providing for such courses. Further research such as a collection of stories embracing positive images of First Nations women involved in technological pursuits should be undertaken to help ameliorate the status of First Nations women in technology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".