Perpetuating precarity in theory and in practice: a case study of work-integrated learning in the non-profit sector in Northern Canada
Bibliographic record
Abstract
Work-integrated learning (WIL) is a process of curricular experiential education within a workplace or practical setting. WIL is portrayed as a win-win, yet this research suggests that WIL perpetuates precarity and deepens inequalities between students, between different types of employers, and between geographic regions. Using the Human Development and Capabilities Approach (HDCA), this study investigated how eight diverse non-profit organisations (NPOs) in northern Canada are positioned to support students to develop personal agency through WIL. Most WIL research is urban-centric, focused on for-profit industries and framed within Human Capital Theory (HCT), making this study an outlier. Using a case study approach underpinned by critical and social realism, this study explores the ways in which WIL enables and constrains the development of agency at individual, social, and institutional levels. The research shows inconsistencies in current approaches to WIL. The increasingly precarious positioning of NPOs within the labour market threatens their ability to offer students (future) decent work. The institutional and policy environments that undergird WIL do not acknowledge the distinctness of non-profit organisations within a neoliberal economy and this makes invisible other dimensions that affect decent work, such as the regulatory environment, collectivisation, and the ‘contracting regime.’
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.043 | 0.023 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 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".