Canadian Journal of Counselling / Revue Canadienne de Counseling /1992, Vol. 26: I 15 Career Paths and Socio-Economic Status
Bibliographic record
Abstract
Few of the sequential models of adult vocational development have undertaken a comparative identification ofcareer paths according to the socio-economic status ofworkers. On the basis of interviews carried out with 786 adults, randomly assigned after stratifying according to three socio-economic classes, nine age strata, two genders and three working sectors, this article presents three vocational trajectories within the three social classes, each comprising nine phases ofworking life. The integrative model has been elaborated on the basis of these results. This article proposes two of its main principles: I) equilibrated integration of adaptive and creative functions; 2) vocational continuity within time-spaces. Resume Peu de modeIes sequentiels du developpement vocationnel ont procede aune identification comparative des cheminements de carriere en fonction du statut socio-economique des travail-leurs. Nous avons donc tente d'etudier ce point a partir d'entrevues realisees aupres de 786 adultes designes au hasard et ce, suite a une stratification effectued selon trois classes socio-economiques, neufstrates d'age, les deux sexes et trois domaines de travail. Cet article presente
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.056 | 0.005 |
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".