Bibliographic record
Abstract
This chapter discusses the socio-cultural implications of normative learning for normalized work. Normative learning for normalized work from workplace e-learning happens through informationalization of roles and skills as well as the convergence of rules and competencies, for a knowledge-based workplace. Workplace ‘form’ is relevant for workplace e-learning, both as a space for doing work and ultimately for undertaking workplace adult education and training. Workplace form designs have progressed in tandem with changes in society be they social, cultural, political, technological, or economic. In the early 21st century, as workplace form designs again advance to accommodate diverse, global workforces and information and communication technologies (ICTs), workplace e-learning too is impacted. The manifestation of workplace form into daily organizational life depends on particular types of values, capabilities, and organizational structures. What becomes clear is that each workplace form design (as a space for work and learning) may lead to the development of several organizational structures (as specific sites for work and learning). Each form of workplace design and organizational structure also comes with inherent value propositions that lead to the development of specific capabilities based around the fulfillment of key success factors. Workplace e-learning and workplace adult education and training are becoming progressively more normative as workplace forms and organizational structures evolve and change. This is happening more often and as a direct consequence of the convergence and informationalization of skills and competencies from organizational structural development and value propositions. All these hold certain ramifications for normative learning; normalized work; the global workforce doing the learning and the work; and, workplace e-learning.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".