CANADIAN INTERNSHIPS IN FAMILY SCIENCE: CURRENT STATUS AND FUTURE
Bibliographic record
Abstract
ABSTRACT. This is a descriptive study of internships in Canadian family science undergraduate programs. In addition to a document review of 18 baccalaureate and certificate-level family science programs in Canada, faculty members representing eight Canadian academic institutions participated in interviews. Thirteen (72.2%) academic programs offered required or elective student placements. While similarities existed in the purpose of placements between academic institutions, various structural components of placements varied, including the type of placements offered, student-placement process, academic requirements related to the placements, student supervision, and faculty resources required. In addition, similarities and differences existed between the results from this study and results from previous studies conducted in the United States. Future research questions are identified. A family policy alternatives education approach (Bogenschneider, 2002) is used to identify seven possible directions for the future development of internships in family science. Pre-professional experience has many names and many purposes. Internships, practica, field experience, cooperative programs, experiential learning, community-based learning, service learning, part-time employment, and volunteer work are among the numerous ways that students can gain practical experience during their undergraduate studies (Bayley, 2004; Gronski & Pigg,
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".