Bibliographic record
Abstract
This article looks at a 1994 Jobs Study report from the Organization for Economic Co-operation and Development (OECD) that presents a disturbing economic development strategy for use by its member countries. The report calls for the creation of two distinct streams of jobs: high-skill jobs that would have high-knowledge requirements; and low-skill, low-wage jobs that would absorb significant numbers of unemployed workers and for which the report advocates regressive ways to ensure workers are desperate enough to take the low-wage jobs. The concept of "lifelong learning" is central to the process of increasing the skills of those in the high-wage jobs, although it is seen solely as an investment in business and in the economies of OECD member countries. This article raises questions about the direction advocated in the report and explores some of the OECD strategies that have been adopted in Canada under the guise of "structural adjustment." The implications of this direction for university continuing education practice are examined and a social policy role for university continuing educators to play to address the issues of work and learning is discussed.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".