Bibliographic record
Abstract
How work is organised is one of the most important factors in determining what skills workers need to do their jobs successfully. Many analysts have argued that recent decades have seen the beginnings of a revolution in work organisation, a revolution that continues and will have ever widening effects in the workforce. No longer will workers be successful if they are able only to complete one small unchanging set of tasks in a workplace that puts together the work of many to produce goods or services. Instead, they will need to be far more flexible, able to fit productively into teams that are formed for specific work tasks or projects that may only be performed once. They will need a new range of skills to negotiate the new, much more changeable, communication-rich and customer-focused world of work. These broad images of change have been expressed in a myriad of ways, with a variety of emphases. They have become almost an article of faith when talking about the likely future of work and skill requirements, often providing the context for various claims. To take one example, a recent NCVER collection on ‘generic skills ’ begins with the assertion that: In today’s economy, knowledge, information, customer service, innovation and high performance are at a premium and generic skills are essential…[for workers] (Gibb and Curtin, 2004, p.7). The implication is clear: ‘today’s economy ’ is different from yesterday’s, and so are the kinds of skills it demands of workers. The purposes of this paper are to take
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".