Effective Career Services Practices: The Case of Canadian Business Schools
Bibliographic record
Abstract
Perceived quality of a business school education is closely tied to student satisfaction with Career Services throughout the course of study. This is true for two reasons. First, students seek assurance that their educational investment will result in a secure future. Second, students often use business school ranks published in high profile magazines and newspapers such as Canadian Business, U.S. News and World Report and Financial Times. A significant percentage of the weight in business school ranks depends upon student and recruiter perceptions of the school’s career centre (CC). In this key informant study, practices used by Canadian business school CCs are reported and presented in the context of theory of best practices and studies of CCs in the US among top performing schools. We find that despite their relative inexperience, rapidly increasing demands, and limited resources, that practices used by Canadian business school CCs are in line with the most successful CCs in the US and consistent with theory of effective practices. Structured telephone interviews were conducted with fourteen directors of CCs in Canadian business schools. Through an analysis of the interview text, five essential themes of the career centre practices emerged.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.062 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".