Bibliographic record
Abstract
What kind of workplace has the so‐called ‘new economy’ created? What problems are Canadian workers experiencing? How effective are Canada’s lifelong learning policies that focus on high skills development for global competitiveness? These questions were explored as part of a three year research program. During the 2003–2004 academic year, using a grounded theory research method and working from a critical perspective, I asked these questions to 11 union organizers and staff representatives. During the 2004–2005 academic year, I interviewed seven academic adult educators and asked for their analysis of the issues raised by the Year 1 research participants, and for feedback on my analysis of the Year 1 data. Three interrelated ‘work’ themes emerged from the interviews with union workers: increased workload, job insecurity, and loss of job satisfaction. Because of the workplace problems associated with the so‐called new economy, the research participants were highly critical of the current focus of lifelong learning. This article explores the work themes and their critique. Building on a worker perspective, this research questions the direction of current Canadian lifelong learning policies. It looks at the barriers adult educators encounter in trying to change these policies and suggests that, if change is to occur, Canadian adult educators must retake their place at the policy table.
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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".