Career Information Practices of Guidance Practitioners
Bibliographic record
Abstract
Although career development theories underline the central role of information in career choice and studies show that guidance practitioners are among the main information sources of people making career choices, the actual information practices of these practitioners in their career interventions remain fragmented. Moreover, the studies on the theme of career choice associating information, information sources and information practices (whether it is among guidance practitioners or individuals in career choice) offer little conceptualization on these notions. In order to fill this gap, an online survey of 330 guidance practitioners in Quebec was conducted to document specifically their career information practices (information sources consulted and categories of career information sought). Statistical analysis show that the main career information sought relates to central elements of career choice (training programs and occupations) and the main information sources consulted are non-human and institutional. In addition, some contextual elements are associated with seeking and selecting certain categories of information and sources. The discussion highlights the importance of digital sources in the information practices of these practitioners, the association between the populations served and the choice of information sources and categories of career information and the role of co-workers as information support on career and beyond.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".