Bibliographic record
Abstract
The success of Canada’s immigration policy is intrinsically tied to employment of an immigrant workforce. Teaching is the fourth largest profession among Canadian immigrants, yet immigrants whose occupations are in education are three times less likely to be employed in their matching profession. Failure to incorporate an immigrant workforce not only affects economic success, but has repercussions for immigrant professional identity. This paper reflects on the development of professional identity for twelve internationally educated immigrant teachers (IETs) seeking to reposition themselves as teachers in the Greater Vancouver area of British Columbia, Canada. Through qualitative interviews and Life Positioning Analysis (Martin, 2013), this research explored the role of significant others in facilitating or impeding IETs’ inclusion into the teaching force and subsequent effects on professional identity development. Language and linguistic abilities emerged as a pervasive theme. Participants found acceptance and validation of their language and cultural differences through the perspectives of the students with whom they came into contact. In contrast, the professional teaching community’s perspectives in regard to accents and language proficiency caused IETs to question their competence and negatively impacted their professional identities. Implications for practice with respect to supporting IETs repositioning are offered.
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 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 | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.168 | 0.026 |
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".