Motivations for the Career Choice of Preservice Teachers in New South Wales, Australia and Ontario, Canada.
Bibliographic record
Abstract
This study investigated and compared the reasons given by 40 preservice teachers in New South Wales (NSW), Australia, and Ontario, Canada, for their career choice, focusing on whether their motivations reflected the marked difference in the status and remuneration of teachers in NSW and Ontario. Preservice teachers participated in in-depth, focused, individual interviews in which they were asked to tell a little about themselves and why they decided to become teachers. The NSW students completed interviews during their first year of enrollment, and the Ontario students completed interviews at the beginning of their second year. Data analysis highlighted four categories of reasons: (1) little sense of personal agency (e.g., teaching by default or chance); (2) teaching as a means of gaining personal agency; (3) teaching as a means of assisting others to gain agency; and (4) self as a reform agent. The NSW student teachers had more diverse motivations than did their counterparts in Ontario, who mostly cited a vocation for teaching. In general, the responses of the Ontario preservice teachers indicated higher perceptions of personal agency than did those of the NSW preservice teachers, possibly a reflection of the higher professional status and salaries accorded teachers in Ontario. (Contains 41 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. MOTIVATIONS FOR THE CAREER CHOICE OF PRESERVICE TEACHERS IN NEW SOUTH WALES, AUSTRALIA AND ONTARIO, CANADA
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".