Reconceptualising the transition from post-secondary education to work
Bibliographic record
Abstract
Educational researchers identify the transition from post-secondary education to the labour market as a critical point for the success of the student, and for society more broadly. This transition is often explored as a distinct phase between education and work that can be assessed based on pre-determined outcomes (i.e. employment, income). From this perspective, it is the responsibility of individual students to effectively commodify themselves and navigate their transition into employment. This focus on individual responsibility fails to question social mobility discourse and current labour market realities that significantly influence transition. In order to re-conceptualise transition, I deconstruct social mobility discourse as the foundation of transition research. Then, I draw on narratives of social service workers in British Columbia, Canada, to complexify transition and allow for more nuanced research. The narratives contradict dominant conceptualisations of transition, critiquing transition as a linear process that can be assessed through economic indicators. Recognising transition as a continual process that is influenced by a multiplicity of factors opens new ways to research. Research exploring the nuance of transition moves away from a deficit-focused, intervention approach focused on students, to critically exploring education, the labour market, and the relationship between school and work.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.054 |
| Scholarly communication | 0.020 | 0.014 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".